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How South Korea Is Using AI to Detect Crypto Market Manipulation

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How South Korea Is Using AI to Detect Crypto Market Manipulation

Key takeaways

  • South Korea is transitioning crypto market surveillance to AI-driven systems, in which algorithms automatically detect suspicious trading activity, replacing manual processes.

  • The new detection model employs a sliding-window grid search technique, scanning overlapping time segments to spot abnormal patterns such as unusual volume surges.

  • Through 2026, the Financial Supervisory Service plans to enhance AI capabilities with tools to detect coordinated trading account networks and trace manipulation funding sources.

  • Regulators are exploring proactive intervention measures, such as temporary transaction or payment suspensions, to freeze suspicious activity early and prevent the withdrawal of illicit gains.

South Korea is advancing its cryptocurrency market oversight by shifting to AI-driven surveillance. Algorithms now perform the initial detection of suspicious activities instead of relying solely on human investigators.

As crypto trading grows faster, more decentralized and increasingly difficult to monitor manually, regulators are leveraging artificial intelligence to identify irregularities and anomalies more quickly.

Central to this evolution is the Financial Supervisory Service’s (FSS) enhanced Virtual Assets Intelligence System for Trading Analysis (VISTA). This upgrade reflects the recognition that traditional, manual, case-by-case probes can no longer keep pace with today’s dynamic digital asset markets.

This article explains how South Korea’s financial regulators are using upgraded AI systems to automatically detect crypto market manipulation, improve surveillance, analyze trading patterns and plan advanced tools. It also explores faster intervention and alignment of crypto oversight with broader financial markets.

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Why South Korea is enhancing its crypto monitoring tools

Crypto markets produce massive volumes of data across exchanges, tokens and timelines. Manipulative tactics such as pump-and-dump schemes, wash trading or spoofing often create sudden bursts that are difficult to detect. Manually identifying suspicious periods in crypto activity has become increasingly challenging at the current market scale. As interconnected trading patterns grow more intricate, automated systems are designed to continuously scan and flag potential issues.

This automation aligns with Korea’s broader effort to strengthen oversight of digital markets, particularly as crypto has become more deeply integrated with retail investors and the overall financial system.

What VISTA does and how the recent upgrade improves it

VISTA serves as the FSS’s primary platform for examining unfair trading in digital assets. In its earlier version, analysts had to specify suspected manipulation time frames before running analyses, which restricted the detection range.

The recent upgrade adds an automated detection algorithm that can independently pinpoint potential manipulation periods without manual input. The system now searches the entire data set, enabling investigators to review suspicious intervals that might otherwise go unnoticed.

According to the regulator, the system successfully identified all known manipulation periods in internal tests using completed investigation cases. It also flagged additional intervals that had been difficult to detect using traditional methods.

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Did you know? Some crypto exchanges process more individual trades in a single hour than traditional stock exchanges handle in an entire trading day, making continuous automated surveillance essential for regulators seeking to monitor real-time risks.

How the automated detection operates

Applying a sliding-window grid search approach, the algorithm divides trading data into overlapping time segments of varying durations. It then assesses these segments for anomalies.

The model scans every possible sub-period, identifying patterns associated with manipulation without requiring investigators to determine where misconduct may have occurred. Examples of such patterns include sharp price spikes followed by rapid reversals or unusual volume surges.

Rather than supplanting human oversight, the model prioritizes high-risk segments, enabling teams to focus on critical windows instead of manually reviewing the entire data set.

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Did you know? In crypto markets, price manipulation can sometimes occur in windows lasting less than five minutes, a time frame too short for most human-led monitoring systems to catch reliably.

Upcoming AI enhancements through 2026

The FSS has secured funding for phased AI improvements through 2026. Key planned features include:

  • Tools designed to identify networks of coordinated trading accounts: These systems aim to detect clusters of accounts acting in sync, a common feature of organized manipulation schemes.

  • Large-scale analysis of trading-related text across thousands of crypto assets: By examining abnormal promotional activity or narrative spikes alongside market data, regulators hope to better understand how attention shocks and price movements interact.

  • Tracing the origin of funds used in manipulation: Linking suspicious trades to funding sources could strengthen enforcement cases and reduce the ability of bad actors to obscure their tracks.

Did you know? Early market surveillance algorithms in traditional finance were originally designed to detect insider trading in equities, not crypto. Many of today’s tools are adaptations of models built decades ago for stock exchanges.

Shift toward proactive intervention in South Korea

South Korea’s AI surveillance push seeks quicker responses. The Financial Services Commission is considering a payment suspension mechanism that could temporarily block transactions linked to suspected manipulation.

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This approach aims to prevent gains from being withdrawn or laundered early. While not yet finalized, it suggests a shift by regulators from reactive to preventive enforcement.

Preemptive actions raise important governance questions around thresholds, oversight and the risk of false positives, issues regulators will need to address carefully.

This crypto-focused initiative parallels efforts in conventional capital markets. The Korea Exchange is implementing an AI-based monitoring system to identify stock manipulation earlier. The idea is to create a unified approach across asset classes, combining trading data, behavioral cues and automated risk assessment.

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Strengths and limitations of AI surveillance

AI-based systems are adept at spotting repetitive, pattern-driven misconduct such as wash trading or coordinated price spikes. They enhance consistency by flagging suspicious behavior even when it occurs in small or short-lived windows.

For exchanges, AI-driven oversight raises expectations around data quality and monitoring capabilities. It also increases cooperation with regulators. With AI models, surveillance becomes continuous rather than episodic.

Traders and issuers should expect greater scrutiny of subtle manipulative patterns that previously evaded attention. While detection begins algorithmically, real-world penalties remain significant.

But automated surveillance has certain limitations. Cross-venue manipulation, off-platform coordination and subtle narrative engineering remain difficult to detect. AI models also require regular evaluation to avoid bias, drift or the flagging of legitimate activity.

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AI tools support, not replace, human investigators.

Shaping of a new enforcement framework

South Korea’s strategy involves AI models built around continuous monitoring, automated prioritization and swifter action. As these systems evolve, balancing efficiency with transparency, due process and accountability will be key.

The implementation of these models will shape not only Korea’s crypto markets but also how other jurisdictions approach regulating digital assets in an era of algorithmic trading and mass participation.

Cointelegraph maintains full editorial independence. The selection, commissioning and publication of Features and Magazine content are not influenced by advertisers, partners or commercial relationships.

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Anthropic Expands Its AI Business in India with Strong Enterprise Uptake

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Nexo Partners with Bakkt for US Crypto Exchange and Yield Programs

TLDR

  • Anthropic has doubled its revenue run rate in India in just four months.
  • Developer adoption of Anthropic’s Claude Code has been a key driver of growth in India.
  • The company has opened an office in Bengaluru to support its expanding operations.
  • Anthropic has formed strategic partnerships to expand its AI solutions in the public sector.
  • Air India is using Claude Code to improve software development and reduce costs.

AI startup Anthropic has seen rapid revenue growth in India, with its AI tools gaining traction across various sectors. The company’s revenue run rate has doubled in just four months, driven by high demand from developers and early government deployments. Anthropic has also expanded its presence in the country by opening an office in Bengaluru.

Strong Developer Adoption in India Drives Revenue Growth

Anthropic’s revenue growth in India is largely due to high adoption rates among developers. The company’s Claude Code has seen increased use in India, a country known for its large pool of tech talent. Developers are leveraging the tool to enhance productivity and speed up software development processes. According to Dario Amodei, CEO of Anthropic, India’s developer-centric culture has accelerated the company’s growth. Amodei noted, “Since my last trip here, the company has doubled its run rate revenue in India.”

The speed at which developers are adopting AI tools in India is a key factor in Anthropic’s success. Unlike other countries where casual consumers also use AI, India’s AI adoption is concentrated in the professional sector. This intense use by developers reflects the country’s focus on productivity and rapid experimentation. In India, AI adoption is characterized by a culture of quickly testing new ideas and moving forward with adjustments if necessary.

Anthropic Expands Partnerships to Support Public Sector Growth

In addition to its success in the developer market, Anthropic has formed several strategic partnerships in India. These partnerships will help expand its AI solutions into the public sector, including education, healthcare, and judicial services. The company’s India team will also provide applied AI expertise to startups and enterprises, assisting them in building and scaling AI-driven solutions.

One of the key enterprise clients, Air India, has adopted Claude Code to improve its software development speed. By integrating AI into its operations, the airline aims to reduce costs and increase efficiency. This collaboration reflects the growing interest in AI-powered tools across industries, as businesses recognize their potential to drive productivity improvements.

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Government agencies in India have also shown interest in using AI technology, further accelerating Anthropic’s growth in the country. The Ministry of Statistics, for example, is working on an AI-powered server for economic data and statistics. Anthropic’s CEO highlighted that such efforts are progressing much faster than similar projects in other countries. He credited India’s entrepreneurial spirit and technical expertise for the rapid pace of adoption.

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Paradigm reframes Bitcoin mining as a grid asset, not energy drain

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Crypto Breaking News

A surge in AI data-center activity has rekindled a long-running energy debate, pitting grid operators and policymakers against critics who warn that massive computing operations threaten power reliability and push up electricity costs in parts of the United States. In this backdrop, a February 2026 research note from Paradigm reframes Bitcoin mining within electricity markets, arguing that it behaves as a flexible demand source rather than a static drain on energy resources. The note, which surveys grid conditions and market signals, estimates Bitcoin’s current share of global energy use at about 0.23% and its global carbon emissions at roughly 0.08%. It emphasizes that the network’s issuance schedule and periodic reward reductions inherently cap long-run energy growth, shaping how miners respond to price signals and competing generators. The analysis by Paradigm’s Justin Slaughter and Veronica Irwin, anchored by a public discussion of energy modeling assumptions, invites a more nuanced view of mining’s role in modern electricity systems, beyond broad environmental comparisons.

Key takeaways

  • Paradigm argues that Bitcoin mining is best viewed as flexible grid demand, adjusting consumption in response to real-time electricity prices and grid stress rather than remaining a fixed, unresponsive load.
  • The note quantifies mining’s slice of the energy pie—about 0.23% of global energy use and roughly 0.08% of global carbon emissions—while noting the long-run growth is economically constrained by the fixed issuance schedule and periodic halving of rewards.
  • Critiques of mining energy use that rely on per-transaction measurements are highlighted as misleading, since energy consumption is tied to network security and miner competition, not transaction volume alone.
  • With increasing AI data-center deployments, several miners are partially pivoting to AI workloads to capture higher margins, reshaping the industry’s profile and demand patterns for power.
  • The policy implication is a shift from alarmist energy comparisons to evaluating mining within the broader electricity market—raising questions about how regulators should model and price flexible demand in grid planning.

Tickers mentioned: $BTC

Sentiment: Neutral

Market context: The conversation sits at the intersection of expanding AI infrastructure, grid reliability concerns, and a broader shift toward demand-side flexibility in electricity markets as crypto miners and traditional energy users alike react to price signals and regulatory frameworks.

Why it matters

The framing offered by Paradigm has the potential to recalibrate how policymakers and market participants think about crypto mining. If mining is treated as a responsive load that can scale up or down with grid conditions, it could be integrated more deliberately into demand-response programs and ancillary-services markets. This view challenges simplistic comparisons that measure energy use in isolation or rely on per-transaction efficiency metrics, which may obscure how miners contribute to grid resilience during periods of surplus or shortage.

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The discussion also taps into a broader industry trend: the repurposing of crypto-era infrastructure to artificial intelligence workloads. As margins in traditional mining shift and data-center economics evolve, several players have begun to reallocate hardware and capacity toward AI processing. The shift has been noted across industry reporting and is reflected in the pathways taken by some miners to pursue higher-margin opportunities while continuing mining activities where economics permit. For example, coverage of the AI-data-center wave highlights how existing facilities and equipment can be adapted to meet surging demand for AI workloads, potentially altering regional power usage profiles and pricing dynamics.

At the core of Paradigm’s argument is the idea that energy modeling should reflect the realities of competitive electricity markets rather than rely on static benchmarks. By foregrounding grid conditions, price signals, and the possibility of demand response, the authors argue that Bitcoin mining’s energy footprint can be contextualized within the wider ecosystem of grid economics. This does not absolve miners of responsibility for energy use, but it suggests a framework in which policy decisions are informed by how mining interacts with supply and demand in real time, including its capacity to absorb excess generation or reduce demand during stress events.

The note also emphasizes that energy use and emissions are not the only metrics at play. Understanding where mining sits on the supply curve—where electricity is produced or curtailed—can illuminate why certain regions attract mining operations at particular times and how these operations might contribute to stabilizing grids during peak periods. In this sense, the narrative shifts from a binary “drain vs. benefit” debate to one about how energy users of all kinds can participate in a more dynamic, price-responsive market environment.

As AI infrastructure expands, the mining ecosystem’s response matters for both regional policy and investor sentiment. The industry’s evolving footprint—toward AI workloads in some cases—could influence where and how power is allocated, how utilities price peak versus off-peak energy, and how regulators design frameworks that accommodate flexible demand. While Paradigm’s conclusions are not universal prescriptions, they provide a structured lens for evaluating mining within electricity markets rather than through narrow environmental comparisons alone. The broader takeaway is a push for more sophisticated, market-responsive energy modeling that accounts for price signals, grid constraints, and the real-world behavior of miners under variable conditions.

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What to watch next

  • Publication and discussion of Paradigm’s February 2026 note and any ensuing responses from policymakers or industry groups.
  • New analyses or grid studies examining the elasticity of mining demand in response to real-time pricing and transient grid conditions.
  • Regulatory activity at state or federal levels addressing crypto-mining energy use, permitting, and integration with demand-response programs.
  • Updates on the mining-to-AI workload transition, including pilot projects and capital reallocation by major miners such as those that have publicly discussed strategic shifts.

Sources & verification

  • Paradigm, “Clarifying misconceptions about Bitcoin mining” (February 2026) – note the energy-use and emissions figures and the discussion of market signals. https://www.paradigm.xyz/2026/02/clarifying-misconceptions-about-bitcoin-mining
  • Discussion of AI data centers and Bitcoin mining’s local resistance in the U.S. referencing grid- and energy-demand concerns. https://cointelegraph.com/news/ai-data-centers-local-resistance-bitcoin-mining
  • Bitcoin mining outlook and profitability shifts in the context of AI-driven infrastructure changes. https://cointelegraph.com/news/bitcoin-mining-outlook-2026-ai-profitability-consolidation
  • Bitcoin miner production data illustrating the scale of winter-storm disruption in the U.S. https://cointelegraph.com/news/bitcoin-miner-output-us-winter-storm-latest-data

Bitcoin mining as flexible grid demand in the AI era

Bitcoin (CRYPTO: BTC) mining is increasingly described as a dynamic, price-driven participant in electricity markets rather than a fixed-energy burden. The February 2026 Paradigm note insists that miners act as flexible loads, changing consumption in response to grid stress or surplus supply. This reframing rests on the premise that energy use is not merely a function of transaction volume; it is tied to network security, miner competition, and how power markets price electricity in real time. In practical terms, mining operations tend to gravitate toward the lowest-cost energy sources, often leveraging off-peak generation or surplus capacity, which enables them to scale demand up or down as conditions warrant. The ability to modulate consumption makes mining responsive to price signals, a characteristic that can be valuable to grid operators seeking to balance supply and demand without relying solely on traditional capacity additions.

AI data centers have accelerated this discussion, as industry coverage highlights shifts in crypto-era infrastructure toward AI workloads in some cases. While Bitcoin mining remains a core use case for many facilities, the broader trend underscores how high-density computing can be repurposed to align with profitability drivers and grid economics. Several traditional mining operators, including Hut 8, HIVE Digital, MARA Holdings, TeraWulf, and IREN, have begun exploring partial transitions toward AI processing, highlighting how portfolio strategy can adapt to evolving margins and demand profiles. The implications for energy policy are meaningful: rather than treating all high-energy activities as equivalent, regulators may consider how to integrate flexible-demand resources into reliability and pricing frameworks while maintaining environmental safeguards.

Paradigm’s argument also emphasizes that energy models should reflect the realities of constrained energy systems. If mining adapts to price signals and grid conditions, its contribution to energy demand may be more volatile but potentially more compatible with markets seeking to absorb intermittent generation or reduce peak demand. The authors point to a broader energy-economics logic: when miners respond to scarcity or surplus, they participate in price formation and help balance the system—an argument that invites policymakers to evaluate mining within the rightsized context of electricity markets and grid resilience rather than through simplistic energy-versus-environment comparisons. The discussion aligns with recent coverage of AI infrastructure’s supercycle, suggesting that the real opportunity lies not in static energy tallies but in understanding how demand shapes and responds to evolving grid dynamics.

Risk & affiliate notice: Crypto assets are volatile and capital is at risk. This article may contain affiliate links. Read full disclosure

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Court Slams BitBoy With Punitive Damages Over Viral Accusations Against Kevin O’Leary

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Court Slams BitBoy With Punitive Damages Over Viral Accusations Against Kevin O'Leary


Armstrong had previously published O’Leary’s private phone number and urged followers to harass him as a supposed murderer.

A United States federal judge has ordered crypto influencer Ben Armstrong, previously known as “BitBoy,” to pay $2.8 million after he failed to defend himself in a defamation lawsuit brought by investor and television personality Kevin O’Leary.

According to court documents, US District Judge Beth Bloom of the Southern District of Florida entered the default judgment on Thursday, while citing Armstrong’s lack of any response during the proceedings. The damages award includes roughly $78,000 for reputational harm, $750,000 for emotional distress, and $2 million in punitive damages.

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Background of the Case

The case stems from a series of posts Armstrong published on X in late March 2025, in which he accused O’Leary and his wife of murder and alleged they paid millions of dollars to cover up their involvement in a fatal 2019 boating collision in Ontario.

Two people were killed when one boat struck another on a lake, but O’Leary was only a passenger and was never charged. His wife, Linda O’Leary, on the other hand, was later acquitted of careless operation of a vessel following a 13-day trial. Armstrong publicly disclosed O’Leary’s private phone number and urged followers to contact him as a “real-life murderer.” These posts prompted a temporary suspension from the platform.

In January 2026, Armstrong moved to overturn the default judgment. He said incarceration and mental health problems prevented his involvement, while sealed filings referenced a bipolar disorder diagnosis. The court rejected the request and noted that Armstrong had been properly served and waited nearly a year before taking action.

Legal Woes

The ruling further expands the list of legal troubles facing Armstrong, who has faced repeated arrests since 2023. He was taken into custody in March 2025 on a fugitive warrant tied to alleged threats sent to a Georgia judge and was arrested again in June 2025 on multiple counts of harassing phone calls.

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Armstrong was removed from the BitBoy Crypto brand in August 2023 after its parent company cited substance abuse concerns, which ended his run as one of the most visible figures in crypto media.

His career was repeatedly overshadowed by controversy, including admissions of paid promotions for failed or fraudulent projects and a high-profile legal dispute with YouTuber Atozy that he ultimately abandoned after a backlash from the crypto community.

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Tokenized Real-World Assets See 13.5% Growth Amid Crypto Market Slump

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TLDR

  • The total value of tokenized real-world assets increased by 13.5% over the past 30 days.
  • Ethereum led the growth in tokenized assets, with a $1.7 billion rise in value.
  • Tokenized US Treasuries and government debt remain the largest category in the market.
  • Institutional participation is growing, with major players like BlackRock and JPMorgan entering the space.
  • Tokenized money market funds are evolving, now serving as collateral in trading and lending markets.

Tokenized real-world assets (RWAs) have seen consistent growth, with the total value of onchain RWAs rising 13.5% over the past month. Despite the broader cryptocurrency market shedding $1 trillion in value, the tokenized asset sector continues to show resilience. The demand for tokenized RWAs, especially among institutional investors, reflects a growing interest in utilizing blockchain for traditional financial products.

Ethereum Leads Growth in Tokenized Assets

Ethereum recorded the highest growth in tokenized asset value, with an increase of $1.7 billion. Other blockchain networks, such as Arbitrum and Solana, followed closely, showing $880 million and $530 million in growth, respectively. The surge in value across these networks reflects the broader adoption of blockchain-based tokenized products.

The rise in Ethereum’s dominance highlights the growing role of the blockchain in asset tokenization. As the blockchain’s infrastructure strengthens, more institutions are entering the space, increasing demand for tokenized products. The growth in tokenized asset issuance has also contributed to the overall market rise.

Tokenized US Treasuries and government debt continue to dominate the tokenized asset space, accounting for over $10 billion in onchain products. These assets have seen continuous inflows, which further support their dominant position. As demand grows, more tokenized government securities are being issued on public blockchains.

The expansion of tokenized government debt demonstrates the increasing appeal of blockchain for settling traditional financial assets. Large institutions such as BlackRock, JPMorgan, and Goldman Sachs have shown active participation in this growing market. Their involvement indicates that tokenized government products are becoming a key focus of institutional investment.

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Institutional Participation Drives Tokenized Asset Growth

The demand for tokenized assets points to deeper institutional participation in the space. Asset managers are increasingly issuing and settling tokenized versions of traditional financial products. Tokenized money market funds, which were once seen as yield vehicles, are now serving as collateral in trading and lending markets.

BlackRock’s move into decentralized finance with the launch of its tokenized US Treasury fund is one of the latest examples of institutional involvement. This shows a shift in how traditional financial institutions are engaging with blockchain technology.

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Mike McGlone Forecasts Bitcoin Price Could Fall to $10,000 Amid Economic Concerns

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TLDR

  • Mike McGlone warns that Bitcoin could drop to $10,000 due to rising recession risks in the U.S.
  • The long-standing “buy the dip” mentality may no longer support risk assets, including cryptocurrencies.
  • McGlone highlights Bitcoin’s volatility and predicts a potential reversion to $56,000 before a possible $10,000 decline.
  • Broader market instability, including low volatility in major stock indices, contributes to the ongoing crypto price decline.
  • Jason Fernandes disagrees with McGlone’s forecast, suggesting a $40,000 to $50,000 price range instead of a collapse to $10,000.

Bloomberg Intelligence’s Mike McGlone has raised concerns about the future of Bitcoin. In a recent analysis, he suggested that the ongoing decline in cryptocurrency prices could signal broader financial stress. McGlone also warned that Bitcoin could revert to as low as $10,000, especially if a U.S. recession becomes more likely.

The analyst observed that the market’s traditional “buy the dip” mentality, which has supported risk assets since 2008, may be losing its strength. McGlone pointed out that the worsening situation in the cryptocurrency market is contributing to broader market volatility. He highlighted several macro indicators suggesting heightened risk conditions in global financial markets.

Bitcoin Price Faces Potential Decline to $10,000

McGlone’s analysis specifically mentions Bitcoin’s vulnerability in the current financial environment. He noted that Bitcoin, which recently fluctuated around $68,800, could continue to struggle. According to McGlone, the cryptocurrency’s decline reflects a broader market breakdown, suggesting that the “buy the dip” mindset may no longer be effective.

He further explained that Bitcoin could fall back toward $10,000 if stock markets continue to weaken. McGlone’s chart comparing Bitcoin to the S&P 500 highlighted how both assets were underperforming. He pointed out that Bitcoin’s volatile nature means it is unlikely to remain above current levels if equity markets experience further instability.

In his analysis, McGlone identified a potential reversion level of $56,000 for Bitcoin. This value corresponds to the 5,600 mark for the S&P 500, adjusted for Bitcoin’s volatility. Beyond this, McGlone predicts that the cryptocurrency could fall further, potentially reaching the $10,000 threshold.

Broader Market Volatility Contributes to Crypto Price Decline

McGlone attributes the ongoing volatility in the cryptocurrency market to broader financial instability. The U.S. stock market’s capitalization relative to GDP is at a century-high, signaling potential bubbles. He noted that the low volatility observed in major stock indices like the S&P 500 and Nasdaq 100 could be masking underlying risks.

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Furthermore, McGlone emphasized the “imploding” crypto bubble and the role of factors like “Trump euphoria” in amplifying market stress. While gold and silver are seeing a resurgence, McGlone believes their rise could eventually spill over into equities. He noted that rising market volatility might further challenge asset prices across the board, including cryptocurrencies.

Contrasting Views on Bitcoin’s Future

While McGlone’s thesis on Bitcoin’s potential fall to $10,000 has drawn attention, it has also faced criticism. Jason Fernandes, co-founder of AdLunam, disagreed with McGlone’s view. Fernandes argued that market excesses can resolve through mechanisms like time, rotation, or inflation erosion, rather than necessarily collapsing.

According to Fernandes, Bitcoin’s price could instead stabilize between $40,000 and $50,000 in response to a macro slowdown. He pointed out that a crash to $10,000 would require more severe conditions, including liquidity contraction and financial stress. Fernandes believes that a true recession, marked by global liquidity drainage, would be needed for such a dramatic decline.

However, McGlone’s analysis continues to gain attention, as it reflects rising concerns over both the cryptocurrency and broader market conditions. His forecast suggests that Bitcoin, along with other risk assets, remains highly susceptible to a changing macroeconomic environment.

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Binance Founder CZ Urges Faster Evolution of Privacy Features in Crypto

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TLDR

  • Changpeng Zhao, founder of Binance, emphasizes that privacy is the most significant unresolved issue in the cryptocurrency industry.
  • Zhao argues that Bitcoin and most cryptocurrencies lack adequate privacy features, leaving users vulnerable to tracking.
  • CZ highlights that blockchain transactions are traceable, especially with KYC practices on centralized exchanges.
  • The Binance founder calls for the development of better privacy infrastructure to enable secure crypto payments while complying with regulations.
  • Binance’s history with privacy coins, such as the delisting of Monero, raises concerns about the exchange’s stance on privacy.

Changpeng Zhao, the founder of Binance, has stressed the importance of privacy in the cryptocurrency sector. He pointed out that most digital assets lack sufficient privacy protections, making users vulnerable in ways traditional currency does not. Speaking on the All-In Podcast, CZ emphasized the need for faster advancements in crypto privacy.

Privacy Concerns for Cryptocurrency Payments

CZ argued that privacy plays a fundamental role in society but is currently inadequate in most cryptocurrencies, including Bitcoin. “Bitcoin was designed to be pseudo-anonymous,” he explained. “But in reality, every transaction on the blockchain can be traced, especially with KYC on centralized exchanges.” This, he noted, exposes users to risks like unwanted tracking, especially in scenarios such as hotel bookings where third parties might gain access to personal information.

He further elaborated on how payment privacy is a significant hurdle as the cryptocurrency industry moves toward mainstream adoption. With major players like AI agents and institutional investors getting involved, the open ledger design of blockchains like Bitcoin remains a challenge. CZ believes that to achieve widespread use, privacy features must evolve to meet the needs of both businesses and consumers.

Binance and Privacy Coins

Despite CZ’s calls for better privacy features, Binance’s own history with privacy coins has been controversial. In February 2024, Binance delisted Monero (XMR), which at the time was the largest privacy coin. This decision came shortly after CZ stepped down as CEO of Binance, and it led to a 17% drop in Monero’s price. Binance has often cited factors such as trading volume and liquidity in delisting assets, claiming it takes action when a coin no longer meets its standards.

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CZ’s comments also raised questions about Binance’s stance on privacy coins like Zcash (ZEC). Last year, Binance included Zcash in a community vote on potential delistings. Zcash’s founder, Zooko Wilcox, raised concerns directly with Binance, highlighting the importance of privacy features in cryptocurrency transactions.

The Need for Widespread Privacy Infrastructure

While privacy coins like Monero and Zcash exist, CZ and industry experts suggest that they are not a complete solution. Nic Puckrin, a digital asset analyst, believes the focus should be on developing broader privacy-preserving infrastructure. Puckrin stressed that the issue isn’t to make payments untraceable but to ensure privacy while staying compliant with regulations. He argued that businesses must adopt these privacy features to enable secure crypto payments.

In the face of these challenges, CZ acknowledged that privacy features are a crucial aspect for crypto’s future. Although law enforcement may seek transparency for security reasons, CZ is confident that privacy can be enhanced without undermining efforts to track bad actors.

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Paradigm Challenges Bitcoin Mining Narrative Amid AI Data Center Boom

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Paradigm Challenges Bitcoin Mining Narrative Amid AI Data Center Boom

The rapid buildout of AI data centers has revived a long-running debate over energy consumption, with critics arguing that large computing operations, including Bitcoin mining, strain power grids and drive up electricity prices.

As Cointelegraph previously reported, the surge in AI data center construction has fueled local resistance in several US regions, with residents and lawmakers raising concerns about power demand and rising electricity costs. Bitcoin (BTC) mining has increasingly been linked to the broader debate over high-density computing infrastructure.

In a recent research note, crypto investment firm Paradigm pushed back on that narrative, arguing that Bitcoin mining is frequently misunderstood and often mischaracterized in public energy debates. Rather than treating mining as a static energy drain, Paradigm frames it as a participant in electricity markets, one that responds to price signals and grid conditions.

Paradigm’s Justin Slaughter and co-author Veronica Irwin also challenge several common assumptions used in energy modeling. For example, they note that some analyses measure Bitcoin’s energy use on a per-transaction basis, even though mining energy consumption is tied to network security and competition among miners, not transaction volume. 

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Other models assume energy production is effectively limitless or that miners will continue operating regardless of profitability, assumptions Paradigm argues are unrealistic in competitive power markets.

According to Paradigm, Bitcoin mining currently accounts for about 0.23% of global energy consumption and about 0.08% of global carbon emissions. Because the network’s issuance schedule is fixed and mining rewards decline about every four years, Paradigm argues that long-term energy growth is constrained by economic incentives.

Source: Daniel Batten

Related: Bitcoin miner production data reveals scale of US winter storm disruption

Bitcoin mining as flexible grid demand

A central pillar of Paradigm’s argument is demand flexibility.

Bitcoin miners typically seek out the lowest-cost electricity, often sourced from surplus or off-peak generation.

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Mining operations can scale consumption based on grid conditions, reducing usage during periods of stress and increasing it when supply exceeds demand. In that sense, Paradigm describes mining as a flexible load, similar to energy-intensive industries that respond to real-time pricing signals.

The debate has taken on new urgency as AI data center expansion accelerates. As Cointelegraph recently reported, some crypto-era infrastructure is now being repurposed to support artificial intelligence workloads, with companies shifting from Bitcoin mining to AI data processing to pursue higher margins. Several traditional Bitcoin miners, including Hut 8, HIVE Digital, MARA Holdings, TeraWulf and IREN, have begun making partial transitions.

By framing mining as responsive demand rather than constant consumption, Paradigm’s report shifts the debate from environmental alarmism to grid economics. The implication for policymakers is that Bitcoin mining should be evaluated within the broader electricity market rather than through simplified energy comparisons.

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Related: The real ‘supercycle’ isn’t crypto, it’s AI infrastructure: Analyst