Hyperliquid sees $123M in liquidations yet rivals stay quiet – Why?

ambcryptoPubblicato 2026-02-09Pubblicato ultima volta 2026-02-09

Introduzione

Decentralized perpetual exchanges show high trading activity, but not all volume is legitimate. Hyperliquid reported $3.76B in volume with $123M in liquidations, indicating real leveraged trading under volatile conditions. In contrast, rivals Aster and Lighter posted similar volumes ($2.76B and $1.81B) but significantly lower liquidations ($7.2M and $3.34M), suggesting their volumes may be inflated. When leverage is real, open interest shifts and liquidations occur during price moves. The discrepancy implies that Aster and Lighter’s activity might not reflect actual market risk, raising questions about incentive structures and reporting practices.

Decentralized perpetual volumes are high, dashboards look impressive, and competition between venues is heating up. But not all volume is created equal.

Here’s what you’re missing.

What happened across DEX perp markets

Data per Coinglass revealed a gap between volume and actual market stress.

Hyperliquid [HYPE] posted $3.76 billion in trading volume, with $4.05 billion in open interest and $122.96 million in liquidations. The activity was consistent with real leveraged positioning being pushed during unstable price action.

By comparison, Aster [ASTER] reported $2.76 billion in volume with $927 million in open interest, but liquidations totaled just $7.2 million. Lighter [LIGHTER] had similar numbers: $1.81 billion in volume, $731 million in open interest, and only $3.34 million in liquidations.

Despite headline volumes close to Hyperliquid’s, liquidation activity on Aster and Lighter was roughly 17 to 37 times smaller.

In perpetual futures, real trading activity leaves a trace

When leverage builds, OI changes. When prices move fast, people get liquidated. You can normally see who’s under pressure pretty clearly.

So when volume jumps, but OI and liquidations barely move, it’s suspicious. If traders were actually putting on real risk, you’d expect to see a lot more liquidations.

Incentives, reporting, and the illusion of demand

Domande pertinenti

QWhat was the total trading volume and liquidation amount for Hyperliquid during the period mentioned?

AHyperliquid posted $3.76 billion in trading volume with $122.96 million in liquidations.

QHow did Aster's liquidation amount compare to its trading volume and open interest?

AAster reported $2.76 billion in volume with $927 million in open interest, but liquidations totaled only $7.2 million.

QAccording to the article, what does a large volume without corresponding liquidations and open interest changes indicate?

AIt is suspicious and suggests that the volume may not represent real trading activity or leveraged risk-taking, potentially creating an illusion of demand.

QWhat was the key difference in market activity between Hyperliquid and its rivals like Aster and Lighter?

ADespite having similar headline volumes, Hyperliquid had significantly higher liquidations ($122.96M) compared to Aster ($7.2M) and Lighter ($3.34M), indicating more real leveraged positioning and market stress.

QWhat platform is cited as the source for the data on decentralized perpetual volumes and market stress?

AThe data is per Coinglass.

Letture associate

Apple Also Has to Pay Rent Now

Apple Pays Rent Too: The Two-Way Flow of "Traffic Tax" and "AI Capability Rent" Between Tech Giants For over two decades, Google has paid Apple an estimated $20 billion annually to remain the default search engine on Safari, a "traffic tax" for a critical user entry point. However, in 2026, the direction of this cash flow partially reversed. Apple agreed to pay Google roughly $1 billion per year to license its Gemini AI models, as Apple's own models reportedly struggled with complex tasks. This creates a unique dynamic: Apple acts as the "landlord" in the established search ecosystem, collecting rent from Google for access. Simultaneously, in the emerging AI arena, Apple becomes the "tenant," paying Google for access to cutting-edge AI capabilities it cannot currently match internally. While Apple claims its new models are "distilled" from Gemini outputs and contain "not a drop" of Google's original code, core dependencies remain. Its knowledge base is refined using Gemini's outputs, and its most powerful cloud model runs on Google's infrastructure. Apple has structured the deal as non-exclusive, allowing it to theoretically switch AI suppliers—a hedge against over-reliance. The future hinges on whether advanced AI models become a commodity (cheap and abundant) or remain a concentrated, scarce resource (expensive and controlled by few). Apple is betting on the former, leveraging its massive device ecosystem to be a powerful, choosy customer. If the latter proves true, its bargaining power could erode. This power dynamic is extending to developers. Apple, Google, and WeChat are all pushing for apps to expose their core functions as standardized "actions" or "intents" that their respective AI assistants (Siri, Gemini, WeChat AI) can directly call. The new scarce resource is no longer just app store visibility, but "being selected by the AI." The currency of "rent" has changed from a 30% revenue share to ceding control over how users interact with an app's functions.

marsbit1 h fa

Apple Also Has to Pay Rent Now

marsbit1 h fa

Missed the SpaceX IPO? WEEX's "First Trade Protection" Lets You Experience US Stock Trading Risk-Free.

With the excitement around SpaceX's recent public listing reigniting interest in the US stock market, Chinese investors face significant challenges accessing compliant and convenient trading channels following regulatory actions against major online brokers. This article explores the available options, highlighting their risks and limitations. Traditional paths for US stock investments remain problematic. Qualified Domestic Institutional Investor (QDII) and Listed Open-Ended Fund (LOF) products, while compliant, suffer from high fees, significant purchase premiums, and a very limited selection of assets. Small, unregulated offshore brokers pose substantial risks, including potential insolvency. While secure, VIP accounts at banks in Hong Kong or Singapore require high minimum deposits (often 1-2 million RMB) and in-person visits, placing them out of reach for most retail investors. The article positions cryptocurrency exchanges, specifically their TradFi (traditional finance on-chain) offerings, as a compelling alternative. Platforms like WEEX are noted for providing access to a wide range of US stocks and ETFs, including SpaceX (SPCXON), through tokenized assets. This method offers advantages such as a single account for both crypto and traditional assets, USDT-based settlement avoiding fiat complexities, flexible leverage, and robust risk management. To attract users, WEEX is promoting a "First Trade Guarantee" campaign. Running from June 15 to July 8 (UTC+8), it features a $30,000 prize pool. Users who trade $500 worth of US stock contracts can qualify for a guarantee on their first eligible trade: 100% loss coverage up to $30 or a 20% bonus on profits up to $30. The campaign is presented as a low-risk opportunity for both crypto natives and traditional investors to experience US stock trading.

marsbit1 h fa

Missed the SpaceX IPO? WEEX's "First Trade Protection" Lets You Experience US Stock Trading Risk-Free.

marsbit1 h fa

How Difficult is Chip Making? A Division Error Costs 475 Million Dollars

How Hard Is It to Make a Chip? A Division Error Cost $475 Million Chip expert Shi Kan, a researcher at the Chinese Academy of Sciences and a popular tech creator, explains the immense challenges of chip development. Chips are foundational to modern technology, but their creation is extraordinarily difficult. The journey from sand to a functional chip involves complex design and manufacturing, but a critical bottleneck is verification—ensuring the design works flawlessly before costly production. A single, undetected bug can have catastrophic consequences, as illustrated by the infamous 1994 Intel Pentium FDIV bug. A flaw in the floating-point division unit forced a recall costing $475 million. Unlike software, chips cannot be easily patched after manufacture, making "first-time success" paramount. However, industry surveys show only 24% of chip projects achieve this; over three-quarters require at least one costly re-spin due to design flaws. Verification has thus become the dominant phase, consuming up to 70% of the design cycle. The core challenge is a "verification impossible triangle" between high performance, good debuggability, and low cost. Exhaustively verifying a modern CPU core could take 15,000 years with software simulation, or 30 years with advanced hardware emulation—timeframes utterly impractical for development. Despite being essential, verification is often seen as unglamorous "dirty work," receiving less academic attention than fields like AI. Shi and his team are tackling this by developing an agile verification research framework called ENCORE, based on FPGA technology, to improve verification efficiency and debug capability. Beyond research, Shi engages in public science communication through long-form video content, aiming to demystify chip technology, AI, and computer science. He argues for the value of pursuing "hard and long-term" endeavors, whether in the meticulous world of chip verification or in creating substantive educational content, believing such sustained effort is likely the right path forward.

marsbit1 h fa

How Difficult is Chip Making? A Division Error Costs 475 Million Dollars

marsbit1 h fa

Trading

Spot
Futures
活动图片