Crypto Prices Rebound Amid Liquidations

TheNewsCryptoPublished on 2026-03-24Last updated on 2026-03-24

Abstract

Crypto prices have rebounded in the last 24 hours, with the total market capitalization rising by 3.48%. Bitcoin is trading around $71,032.59, with expectations to approach $76k, while Ethereum remains relatively flat near $2,158.29. Among top gainers, TAO surged 9.75% to $302.75, followed by RENDER and FIL. Despite the recovery, over $203 million in liquidations occurred, with BTC and ETH accounting for the majority. Meanwhile, Gold and Silver also saw gains, reaching record and multi-year highs, respectively.

Crypto prices have rebounded over the last 24 hours. This comes at a time when liquidations are above $200 million at the time of writing this article. Gold and Silver prices have recovered as well; however, crypto prices are at the center with a possibility to move higher.

Crypto Prices

The collective market cap is up by 3.48%. But, crypto prices are under the light with top tokens like BTC and ETH noting decent upticks. Bitcoin tokens are anticipated to move towards $76k, if not surpass it, in the days to come. For now, it is hovering around $71,032.59, up by 0.79%. ETH has largely remained flat at around $2,100. The current exchange value is $2,158.29, up by 0.17%.

TAO stands out as the AI cryptocurrency has gained 9.75% of value to reach $302.75. It goes on to reflect a weekly uptick of 8.53%. RENDER and FIL are the next notable gainers in the segment. They are up 4.42% and 2.28%, respectively.

Meme coins seem to be lagging behind, even though the likes of DOGE and SHIB have added 0.49% and 1.40% to their respective values.

Crypto Liquidations

Crypto prices commenced upticks roughly when US President Donald Trump announced a 5-day temporary pause in strikes. They have maintained the stance since then. What has happened on the sidelines is the liquidation of around $203.03 million in the last 24 hours. Longs stand at $101.51 million, and shorts come to approximately $101.45 million at the moment.

BTC liquidation comes to a total of $154.20 million, with $121.72 million in short and $32.48 million in long. ETH is next on the list with a small gap below the flagship cryptocurrency. Its combined liquidation comes to $129.52 million, with shorts leading at $94 million and the remaining $35.52 million accounting for longs.

Recovery for Gold and Silver

Gold and Silver were reported to be falling behind BTC and other cryptocurrencies. The trend may start to change, given that Gold and Silver prices are now gaining traction. Gold has jumped by 0.41%, a small margin, but now has a record value of $4,422.95. Silver has surged by 1.66%, outperforming Gold, to $70.34.

Meanwhile, Crude Oil and Brent are reportedly carrying the respective values of $90.56 and $101.96 when the article is being drafted.

Highlighted Crypto News Today:

BitGo has Rolled Out AI-Ready MCP Server

TagsCrypto PriceLiquidation

Related Questions

QWhat is the total amount of crypto liquidations mentioned in the article?

AThe total crypto liquidations mentioned are $203.03 million in the last 24 hours.

QWhich AI cryptocurrency gained nearly 10% in value according to the report?

ATAO (Bittensor) is the AI cryptocurrency that gained 9.75% in value, reaching $302.75.

QWhat are the current price levels for Bitcoin and Ethereum as stated in the article?

ABitcoin is hovering around $71,032.59 and Ethereum is at $2,158.29.

QHow did the prices of Gold and Silver perform, and what are their new values?

AGold jumped 0.41% to a record $4,422.95, and Silver surged 1.66% to $70.34.

QWhat event does the article suggest contributed to the commencement of crypto price upticks?

AThe article suggests that crypto prices began to rebound roughly when US President Donald Trump announced a 5-day temporary pause in strikes.

Related Reads

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

AI is reshaping the labor market's value proposition. The traditional four-year college degree is losing its appeal as a guaranteed career path, while skilled blue-collar trades like electricians, welders, and plumbers are experiencing historic demand and wage premiums. This shift is driven by dual pressures: AI's displacement of certain white-collar roles and a booming need for physical infrastructure and data center construction. Data confirms the trend. In the U.S., vocational school revenue surged, and a significant portion of recent layoffs are AI-related. Surveys show a majority of Gen Z adults plan to pursue blue-collar work, citing better job security against AI automation. Vocational education interest has exploded recently. Experts cite a psychological shift as younger generations seek tangible, AI-resistant careers and avoid high student debt. In many cases, salaries for skilled trades now match or exceed those requiring a bachelor's degree. In South Korea, semiconductor vocational high schools boast near-total employment, with graduates securing high-paying roles at companies like Samsung. The shortage is structural, exacerbated by a retiring baby boomer workforce and massive infrastructure projects. Companies like JPMorgan Chase, Meta, and Lowe's are investing heavily in training programs. However, overcoming historical stigma and a "perception gap" around trade careers remains a key challenge to closing the talent gap.

marsbit27m ago

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

marsbit27m ago

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

Qualcomm reported its Q3 FY2026 results (ending June 2026), with revenue of $9.95B, down 4% YoY but above expectations. Gross margin declined to 53.1%, pressured by rising costs across manufacturing and memory. Key business segments showed mixed performance: Handset revenue fell 19.6% YoY to $5.09B, dragged by an 11% decline in non-Apple Android shipments and weaker high-end mix. Conversely, Automotive revenue surged 61% to $1.59B, and IoT grew 9% to $1.83B. Core operating profit dropped 41% YoY due to margin compression and higher expenses. Management's Q4 FY2026 guidance projects revenue of $9.7B-$10.5B, in line with consensus, but Non-GAAP EPS guidance of $2.05-$2.25 fell short of expectations. Amidst persistent weakness in its core handset market, Qualcomm is pursuing growth in AI, focusing on Edge AI (smartphones, PCs, automotive) and Data Center AI. Its data center strategy includes four pillars: AI accelerators (e.g., AI200), commercial CPUs (Dragonfly C1000), custom silicon, and connectivity solutions. While these initiatives initially boosted its stock, concerns over AI capital expenditure sustainability have since erased those gains. The company targets $5B in data center revenue for FY2027 and $15B for FY2029. The report concludes that with the traditional handset business still under pressure, the data center opportunity is currently viewed as a longer-term option, and a more conservative valuation based on core operations may be warranted until AI contributions materialize.

marsbit32m ago

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

marsbit32m ago

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

At the 2026 YC Startup School, Jeff Dean outlined his vision for AI's next phase, shifting focus from simply scaling models to building intelligent, autonomous systems. He believes AI's progress is no longer just about creating smarter models, but about integrating them into systems capable of long-term, iterative work, automated experimentation, and continuous learning. This evolution moves the competition from "who has the bigger model" to "who can best organize intelligence." Dean suggests AI capabilities are now comparable to a junior engineer, enabling the automation of complex workflows. However, the true challenge and opportunity lie in managing these AI "workers" at scale. He emphasizes the importance of **context engineering**—structuring tools, memory, and feedback loops—over raw model power. For startups, this means building deep expertise in niche domains where general models currently fail (near 0-1% success rates), leveraging proprietary data, specialized tools, and domain-specific evaluators. A recurring theme is re-examining fundamental constraints. Dean's past work, like moving Google's search index to memory or creating the TPU, stemmed from questioning outdated assumptions about hardware and cost. He sees similar inflection points today, particularly in **specialized inference hardware** to drastically reduce latency and energy consumption for real-time Agent operation. Notably, he points out that in modern AI systems, the dominant cost is often not computation but **data movement**. Reliable, long-running Agents require robust system design, borrowing concepts from distributed computing like checkpointing, state management, and parallel exploration to handle failures and maintain progress over days or weeks. As AI automates execution, the scarcest human skills will shift to **defining clear specifications**, **judging what problems are worth solving** (taste), and designing effective feedback loops. Ultimately, Dean's framework prioritizes understanding the problem deeply, identifying the true bottlenecks, and systematically building closed-loop systems where AI can not only perform tasks but also improve AI itself.

marsbit32m ago

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

marsbit32m ago

Trading

Spot
活动图片