Crypto markets slide as Bitcoin dips below $93K amid liquidations and tariff-driven uncertainty

ambcryptoPublicado em 2026-01-19Última atualização em 2026-01-19

Resumo

Crypto markets declined as Bitcoin fell below $93K, triggering over $602 million in long liquidations. Major tokens like Ethereum and Solana also dropped 2-3%, with total market cap down over 2% to $3.14T. Thin holiday liquidity in the U.S. and macro uncertainty—driven by tariff rhetoric and geopolitical tensions—amplified the sell-off. Market sentiment turned neutral (Fear & Greed Index at 45), but Bitcoin remains above key long-term supports. The pullback appears driven by leverage unwinds and external risk factors rather than fundamental weakness.

Crypto markets moved lower as risk sentiment across financial markets softened. This pushed major tokens into the red, triggering leveraged liquidations, according to real-time price data and sentiment indicators.

As of the latest market prices, Bitcoin [BTC] is trading near $92,900. It is down about 1% in the past 24 hours, with volatility evident around key support levels after recent resistance.

Ethereum [ETH] is quoted around $3,200–$3,220, down by over 2%. Also, Solana [SOL] is in the $130–$145 range, down by over 3%.

The decline reflects broad weakness across large-cap altcoins, as measured by live price feeds.

The total capitalization of the crypto markets is roughly $3.14 trillion, down over 2% on the day. Trading volumes remain elevated at over $120 billion.

Crypto markets liquidations rise on price pullback

A wave of leveraged liquidations across crypto derivatives markets has accompanied the recent price deterioration.

Multiple data sources show that hundreds of millions of dollars in long positions were closed out over the past 24 hours.

Data from Coinglass showed over $602 million in long liquidations, with significant activity concentrated in Bitcoin and Ethereum markets.

These automated liquidations typically occur when leveraged bets on price rises fail to hold support levels, contributing to short-term downward pressure.

Thin liquidity amplifies macro-driven moves

The downturn unfolded in a thinner liquidity environment, with U.S. equity markets closed for the Martin Luther King Jr. Day holiday, while crypto markets continued trading uninterrupted.

Historically, such conditions can exaggerate price moves in crypto, particularly when combined with elevated leverage.

At the same time, renewed tariff rhetoric and geopolitical uncertainty added to risk-off positioning across global markets.

Recent statements from U.S. President Donald Trump signalling potential tariff action against Europe, alongside broader tensions over Iran and Greenland, weighed on investor sentiment, even in the absence of immediate policy changes.

Traditional markets reacted cautiously, with equity futures under pressure and safe-haven assets such as gold attracting flows.

Crypto markets, which often act as a high-beta risk asset in the short term, reflected that shift through accelerated liquidations and broad-based declines.

Crypto markets sentiment turns cautious

Market sentiment indicators continue to reflect caution. Alternative live sentiment indices show mixed fear and neutral readings across major tokens, with several assets still classified in neutral or fear territory, indicating tepid conviction among traders.

As of this writing, the Fear and Greed Index, according to CoinMarketCap, was 45, indicating a neutral sentiment.

Despite the pullback, Bitcoin continues to trade well above key longer-term support zones established earlier in the cycle, leaving the broader structure intact for now.

However, sustained weakness below current support levels could invite further downside if macro uncertainty persists and liquidity remains constrained.

Final Thoughts

  • The latest crypto sell-off reflects a leverage-driven unwind exacerbated by thin liquidity and renewed macro uncertainty rather than a fundamental shift in market structure.
  • With geopolitical headlines and tariff risks back in focus, short-term volatility is likely to remain elevated until clearer signals emerge from broader markets.

Perguntas relacionadas

QWhat was the main reason for the recent downturn in crypto markets according to the article?

AThe downturn was caused by a combination of softened risk sentiment across financial markets, a wave of leveraged liquidations, and renewed macro uncertainty driven by geopolitical tensions and potential tariff actions.

QWhat was the approximate total value of long positions liquidated in the last 24 hours, as reported by Coinglass?

AData from Coinglass showed over $602 million in long liquidations.

QHow did the U.S. Martin Luther King Jr. Day holiday contribute to the market conditions?

AThe holiday resulted in thinner liquidity as U.S. equity markets were closed, which historically exaggerates price moves in crypto markets, especially when combined with high leverage.

QWhat reading did the Fear and Greed Index show, and what does it indicate?

AThe Fear and Greed Index was at 45, indicating a neutral market sentiment.

QWhich two major geopolitical factors are mentioned as adding to the risk-off sentiment?

AThe two factors mentioned are potential tariff actions against Europe signaled by former U.S. President Donald Trump and broader tensions concerning Iran and Greenland.

Leituras Relacionadas

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

Silicon Valley investor and "Godfather of Startups" Steve Hoffman warns that combining Web3 with AI is likely a trap, not a promising venture. In an interview, Hoffman argues that while AI is a foundational technology touching all industries, Web3 adds complexity, friction, and regulatory risk without solving mainstream consumer or business needs. He advises founders to focus on deep, specialized applications where startups can out-iterate giants, rather than on generic features easily replicated by large tech companies. Hoffman observes that Silicon Valley will lead foundational AI research, while China excels at rapid, large-scale application and commercialization, particularly in robotics. He stresses that AI-driven autonomous agents capable of collaborative, multi-step tasks are 2-4 years away, which will cause significant job displacement. The solution is not to slow AI but to redesign business models around human-AI collaboration and reform social systems like education and retraining. For startups, Hoffman recommends focusing on vertical, expertise-heavy domains to build defensibility. He sees major opportunities in AI fraud detection and cybersecurity. Key founder mindsets include systemic thinking over feature-focus, relentless customer centricity, building adaptive teams, and deeply understanding AI's capabilities and limits. Hoffman is also leading a non-profit initiative to establish university centers aimed at training future leaders in responsible, human-value-aligned AI innovation.

marsbitHá 2m

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

marsbitHá 2m

Token Inefficient, Economy Tokenless

The article "Tokens Aren't Economical, Economics Aren't Tokenized" analyzes a pivotal shift in the AI industry from a technology-driven narrative to one dominated by capital efficiency. It highlights two concurrent trends: a severe capital shortage due to the exorbitant and recurring costs of compute (e.g., OpenAI's high burn rate) and a wave of corporate spin-offs where major tech companies are separating their AI units (like Kuaishou's Kling and Baidu's Kunlunxin). The core argument is that AI's "anti-internet" business model, where user growth increases costs rather than profits, has created a disconnect between high valuations and actual cash flow. Spin-offs address this by allowing AI assets to be valued independently. Within a parent company, they are seen as cost centers, but as standalone entities, they are priced based on their growth potential and scarcity in the primary market, leading to massive valuation premiums (e.g., Kling's estimated value tripling post-spin-off). The industry is at an inflection point, moving from "model worship" to "value realization." The competition is evolving from a pure compute (GPU) race to a broader focus on systemic efficiency and full-stack engineering (involving CPUs and orchestration) to achieve viable commercialization. The year 2026 is framed as a critical moment where the industry must definitively answer how to economically translate AI capability into tangible business value, reshaping the sector's future power structure.

marsbitHá 7m

Token Inefficient, Economy Tokenless

marsbitHá 7m

Crossing the 'Memory Wall': The Wafer-Level Revolution and Computing Power Routes in the AI Inference Era

In 2026, a historic shift occurred in AI as major cloud providers' inference spending surpassed training spending for the first time, signaling a move from "building large models" to "using large models." This shifts the core challenge from computing power to the "memory wall"—the bottleneck of data movement (model weights, activations, KV Cache) between external DRAM and processors, where energy and latency from data transfer far exceed computation itself. Companies like Nvidia face GPU idle time due to bandwidth limits. In contrast, Cerebras Systems adopts a radical "wafer-scale" approach with its Wafer-Scale Engine (WSE). Instead of cutting a silicon wafer into many chips, Cerebras uses almost the entire wafer as one massive chip (WSE-3). This design provides 44GB of on-chip SRAM, delivering memory bandwidth thousands of times higher than traditional HBM (e.g., 21 PB/s vs. Nvidia B200). For LLM inference, weights are streamed layer-by-layer from external MemoryX storage to the chip, avoiding HBM bottlenecks. This results in token generation speeds 1.5–5 times faster than Nvidia's B200 in some models and significant advantages in first-token latency and long-context tasks. Additionally, Cerebras's architecture offers much lower interconnect power consumption (0.15 pJ/bit vs. GPU's ~10 pJ/bit). However, Cerebras faces challenges: SRAM scaling has slowed with advanced nodes, limiting future capacity gains; the chip requires specialized liquid cooling and custom software stacks; and its external I/O bandwidth (150 GB/s) is low compared to NVLink, hindering multi-system scaling for very large models. Competition is intensifying. Major players are pursuing three paths: 1) Developing proprietary inference ASICs (e.g., Google TPU, Microsoft Maia), 2) Leveraging advanced packaging (e.g., TSMC's SoW) to democratize wafer-scale-like integration, potentially eroding Cerebras's process advantage within a few years, and 3) Exploring optical interconnects for ultimate bandwidth. Commercially, Cerebras is transitioning from a hardware vendor to a service provider, facing the immense challenge of building high-power, specialized data centers to meet large contracts (e.g., 250MW/year from 2026–2028). In conclusion, the AI inference era presents a fundamental architectural trade-off. Cerebras opts for extreme physical optimization for low-latency, single-task performance, while Nvidia prioritizes versatility and massive cluster throughput. The path forward remains uncertain, with technology and business models still evolving in the race toward advanced AI.

marsbitHá 13m

Crossing the 'Memory Wall': The Wafer-Level Revolution and Computing Power Routes in the AI Inference Era

marsbitHá 13m

Has Bitcoin's 'Rebound Ended', Officially Entering the Late Bear Market Phase?

**Title: Has Bitcoin's Rebound Ended, Entering the Late Bear Market Phase?** **Summary:** Bitcoin's price has declined by 13% this week, signaling a potential return to late-stage bear market conditions. The price fell to around $67k, positioned between the Realized Price and Realized Cap Weighted Average. For the first time since early 2022, the Short-Term Holder cost basis has dropped below this key average, confirming a hallmark of late-cycle bear markets. Profitability metrics have collapsed sharply. The 7-day average of the Realized Profit/Loss ratio plummeted from a local high of 3.16 to 0.29, mirroring the February panic sell-off. Critically, the 90-day average never breached the threshold of 2, indicating the recent rally to $82k was a bear market bounce, not a structural shift. Realized losses surged to $1.35 billion daily, with $770 million coming from Long-Term Holders selling at a loss. This accelerating redistribution of supply from weak to strong hands is a necessary but ongoing process for a market bottom. The rally stalled almost precisely at the aggregate cost basis (~$83k) of US spot Bitcoin ETF investors, turning that level into strong resistance and leaving the average ETF holder underwater again. Spot market flows have turned decisively negative, showing sellers are dominating order books despite the price drop. While a significant futures long liquidation event cleared over $400 million in leverage, providing a potential reset, sustained spot demand is yet to materialize. Options markets continue to price in higher future volatility (Implied Volatility) than recent price action (Realized Volatility) has shown, with a persistent skew towards put options, indicating ongoing demand for downside protection. In conclusion, multiple metrics point to a fragile market structure. Resistance at the ETF cost basis, accelerating realized losses, dominant spot selling, and cautious options pricing all suggest the bear market trend persists. A sustainable recovery likely requires a resurgence of spot demand, ETF holders returning to profit, and a clear reduction in selling pressure.

marsbitHá 13m

Has Bitcoin's 'Rebound Ended', Officially Entering the Late Bear Market Phase?

marsbitHá 13m

TechFlow Intelligence Agency: Anthropic Calls for Global Pause in AI Development While Preparing for Trillion-Dollar IPO; SpaceX IPO Roadshow Heats Up, But S&P 500 Rejects Fast-Track Inclusion

In today's TechFlow Intelligence Briefing, several major tech stories highlight a growing theme of trust and credibility gaps across AI, crypto, and finance. AI company Anthropic has publicly called for a global pause in AI development, citing risks from Claude's "recursive self-improvement." Ironically, this coincides with reports the company is preparing for a massive IPO targeting a near $1 trillion valuation. This perceived hypocrisy, coupled with widespread user complaints about Claude's declining performance, is sparking debate over whether the safety warning is genuine or a competitive tactic. Meanwhile, in a substantive security move, Anthropic open-sourced a framework for AI-powered vulnerability discovery. In the crypto market, Bitcoin's price drop below $61,000 triggered over $1.16 billion in liquidations, flipping the market into a state where more BTC is held at a loss than at a profit, a historical bearish signal. On the corporate front, SpaceX's highly anticipated IPO is generating immense Wall Street excitement, with Goldman Sachs projecting 100x revenue growth by 2030. However, the S&P 500 has refused to fast-track the company's inclusion post-IPO, potentially limiting immediate institutional demand. Separately, ByteDance's AI app Doubao lost over 6 million monthly active users after introducing a subscription model, highlighting the challenges of AI monetization. Other notable developments include Nvidia certifying HBM4 memory from Samsung, SK Hynix, and Micron; Cloudflare's acquisition of front-end tooling company VoidZero; and its CEO warning that bot traffic now exceeds human traffic online. The underlying narrative connects these events: a trust crisis. From AI firms' contradictory actions and crypto volatility to the clash between SpaceX's hyped narrative and institutional rules, a pattern is emerging where stated intentions and actual practices are increasingly misaligned.

marsbitHá 28m

TechFlow Intelligence Agency: Anthropic Calls for Global Pause in AI Development While Preparing for Trillion-Dollar IPO; SpaceX IPO Roadshow Heats Up, But S&P 500 Rejects Fast-Track Inclusion

marsbitHá 28m

Trading

Spot
Futuros
活动图片

Categorias popularesright-arrow

Etiquetas Popularesright-arrow