40% altcoins near cycle lows, but here’s why THESE 22 tokens are NOT among them!

ambcryptoPublicado em 2026-03-31Última atualização em 2026-03-31

Resumo

The crypto market has declined over the past six months, with total valuation down 45% from its peak. According to a CryptoQuant report, nearly 40% of altcoins are trading near or at all-time lows, reflecting deeper stress compared to the previous cycle. Broader macro pressures, including inflation and geopolitical tensions, have contributed to the downturn. Additionally, capital dilution has intensified due to the expansion of cryptocurrencies, fragmenting liquidity. Despite the weakness, select altcoins have shown resilience. The 90-day Altcoin Season Index remained relatively firm, with 22 altcoins in profit during this period. Top performers include Hyperliquid (HYPE), MemeCore (M), Canton Network (CC), and Bittensor (TAO), with gains of 47%, 66%, 14%, and 44%, respectively. Categories like bridge tokens, AI-related assets, utilities, social tokens, and exchange tokens were among the least affected. The accumulation/distribution indicator suggests buying activity is underway, but this may be short-term relief rather than a confirmed reversal. A breakout above the descending resistance line is needed to confirm building demand and potential broader market recovery.

The crypto market has extended its decline over the past six months, with little evidence of a sustained rebound on the charts. With total market valuation down about 45% from its peak above $4.27 trillion, a growing number of altcoins are now sitting near their historical lows.

This compression has reduced the number of assets positioned to benefit meaningfully from a recovery, concentrating potential upside in a smaller subset of tokens.

Altcoins approach cycle extremes

According to a recent CryptoQuant report, nearly 40% of altcoins have either reached their all-time lows or are now trading within close range of those levels.

This downturn is primarily a product of broader macro pressures, including persistent inflation concerns tied to oil markets and escalating geopolitical tensions across West Asia.

Relative to the previous cycle, current conditions are slightly more severe though. During the last downturn, about 38% of altcoins fell to or near all-time lows before establishing a base and reversing higher. The current figure exceeds that level by 2%, highlighting deeper market stress.

At the same time, capital dilution has intensified too. The number of cryptocurrencies has expanded to roughly 47 million, with about 44 million concentrated across Solana, Base, and BNB. This surge in token supply has fragmented liquidity, spreading capital thinly across an increasingly crowded market.

Despite the broader weakness, however, select sectors and tokens have continued to outperform lately – A sign that they may attract disproportionate capital if market conditions stabilize again.

Select altcoins show resilience

Consider this – The 90-day Altcoin Season Index, which tracks performance outside Bitcoin, has held relatively firm despite the broader market’s decline.

Within this period, 22 altcoins have been in profit, positioning them as key assets drawing investor attention.

Leading performers include Hyperliquid [HYPE], MemeCore [M], Canton Network [CC] and Bittensor [TAO], with these alts up 47%, 66%, 14% and 44%, respectively.

AMBCrypto further grouped these assets by three-month performance to identify segments likely to attract capital.

Artemis year-to-date data from January through March revealed that bridge tokens, AI-related assets, utilities and services, social tokens, and exchange tokens have been among the least affected categories.

Signs of accumulation, but confirmation remains key!

At the time of writing, the accumulation/distribution (A/D) indicator suggested that broad-based buying activity was underway, with trading volume hitting approximately 4.5 billion tokens.

However, this accumulation may reflect a short-term relief phase rather than a confirmed reversal. Especially since the price action remains under a descending resistance line.

A breakout above this level, followed by sustained upward movement, would confirm building demand. Such a shift could drive broader market recovery and amplify momentum in the market’s best performers this quarter.

Final Summary

Roughly 40% of altcoins are trading near their all-time lows, with many already deep in losses.
Despite the broader downturn, 22 altcoins have sustained bullish momentum over the past 90 days.

Perguntas relacionadas

QAccording to the CryptoQuant report, what percentage of altcoins are trading near their all-time lows?

ANearly 40% of altcoins have either reached their all-time lows or are trading within close range of those levels.

QWhich four altcoins are mentioned as leading performers with significant gains in the last 90 days?

AHyperliquid [HYPE], MemeCore [M], Canton Network [CC], and Bittensor [TAO] are the leading performers, up 47%, 66%, 14%, and 44% respectively.

QWhat are the five token categories that have been least affected by the market downturn according to Artemis data?

ABridge tokens, AI-related assets, utilities and services, social tokens, and exchange tokens have been among the least affected categories.

QWhat does the Accumulation/Distribution (A/D) indicator suggest is happening in the market at the time of writing?

AThe A/D indicator suggests that broad-based buying activity is underway, with trading volume hitting approximately 4.5 billion tokens.

QWhat key condition is needed to confirm a building demand and potential broader market recovery?

AA breakout above the descending resistance line, followed by sustained upward movement, would confirm building demand and could drive a broader market recovery.

Leituras Relacionadas

How Likely is it for TradeXYZ to Go Solo from Hyperliquid?

The article analyzes the possibility of TradeXYZ, a dominant project on Hyperliquid's HIP-3 real-world asset (RWA) perpetual contracts market, splitting off to build its own independent trading platform. With TradeXYZ commanding over 90% of HIP-3 volume and over 35% of Hyperliquid's total open interest, its leverage and influence are significant. The primary motive for a split is seen as capturing the underlying protocol fees, of which TradeXYZ currently receives only half. However, several factors strongly discourage such a move. First, replicating Hyperliquid's high-performance infrastructure would be challenging and could degrade TradeXYZ's product quality. Second, Hyperliquid serves as a crucial distribution channel, with most users accessing TradeXYZ's liquidity through its frontend. Third, the close, trusting relationship between the founders of both projects makes a hostile "betrayal" unlikely. The conclusion is that a split would likely result in a lose-lose scenario. Hyperliquid would lose its main growth narrative and see its valuation impacted, while TradeXYZ would face immense technical hurdles, lose its primary user channel, and damage its reputation. Instead of a full separation, the more probable path is for TradeXYZ to negotiate better terms while deepening integration, focusing on enhancing its own brand and user ownership.

marsbitHá 12m

How Likely is it for TradeXYZ to Go Solo from Hyperliquid?

marsbitHá 12m

"Teletubbies" Robot Cleaning Service, $30/Hour, Pure·Manual·Intelligence

Anthropomorphic "Teletubby" robot offers cleaning services in San Francisco at $30/hour, but it's entirely remote-controlled. The robot, created by startup Tau Robotics, can perform household tasks like washing hands, mopping floors, and taking out trash. While the initial demo videos appear impressive and are notably shown at normal speed (unlike many sped-up robot demos), the company reveals the actions are performed via human teleoperation, not autonomous AI. Tau Robotics, founded in 2024, argues this "cheat" is a strategic way to bridge the current capability gap, ensure task completion, and collect real-world home data to eventually train autonomous systems. Their service features three robot models: Chelsea for kitchens/bathrooms, Elon for regular tidying with memory, and Tony for deep cleaning. Priced at $30 per hour, it's cheaper than average human cleaners in the US. The article discusses the broader challenge of deploying humanoid robots in homes, comparing Tau's approach to others like China's Ziliang and the US's 1X Neo, which also use teleoperation. A key reason for choosing a humanoid form is to make remote control more intuitive for human operators. The piece also notes the potential "emotional value" of having a humanoid servant. The service is currently invite-only in San Francisco.

marsbitHá 26m

"Teletubbies" Robot Cleaning Service, $30/Hour, Pure·Manual·Intelligence

marsbitHá 26m

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.

marsbitHá 1h

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

marsbitHá 1h

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.

marsbitHá 1h

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

marsbitHá 1h

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.

marsbitHá 1h