3 Reasons Why Some Ethereum Whales Constantly Buy Shiba Inu Tokens

u.todayPublished on 2022-04-21Last updated on 2022-04-21

Abstract

According to WhaleStats, Shiba Inu remains the biggest USD-valued position among Ethereum whales with large wallets holding approximately $1.3 billion worth of SHIB.

Contents

Whales are trying to take control over the token

A massive discount

Shiba has the "meme potential"
Following another large purchase of 50 billion Shiba Inu tokens, it is important to break down why exactly Ethereum whales or simply large cryptocurrency market participants are constantly buying up to trillions of SHIB tokens worth millions of dollars.
Whales are trying to take control over the token
According to WhaleStats, Shiba Inu remains the biggest USD-valued position among Ethereum whales with large wallets holding approximately $1.3 billion worth of SHIB. The desire of whales to take control over the asset's circulation on the market explains such strong dominance.

Shiba Inu Data

Source: WhaleStats Usually, a high concentration of supply in the hands of whales is considered "bullish" by crypto enthusiasts, as they prefer accumulating assets over redistributing them. With a high percentage of the supply being held by retail traders and investors, the market usually sees more selling pressure than in the other case.
A massive discount
While taking control of the circulating supply, whales would not have been actively buying an asset with such a large discount as  the one we see on Shiba Inu. According to TradingView data, SHIB lost more than 70% of its value since the ATH, which makes the Risk/Reward ratio in case of another rally appealing for investors.
Shiba has the "meme potential"
The meme's potential term was brought up on the market after Dogecoin's massive run in 2021, which usually means that the asset has the potential for a strong rally with no fundamental reasons behind it.
"Meme rallies" are usually fueled by retail traders and last only a couple of weeks in the best-case scenario. While this kind of rally bring hundreds of percent to investors and traders, they tend to quickly fade out and usually lose up to 90% of their value.

Related Reads

From Ethereum to AI's 'CROPS': What Exactly is This Set of 'Slow Variables' That Vitalik Repeatedly Emphasizes?

In recent discussions, Vitalik Buterin has frequently emphasized the concept of "CROPS," a framework defining core values for Ethereum's development. CROPS stands for Censorship Resistance, Capture Resistance, Open Source, Privacy, and Security. Initially outlined in the Ethereum Foundation's "EF Mandate," it represents a commitment to user sovereignty, ensuring that the network resists external control, remains open, protects privacy, and prioritizes security. The relevance of CROPS extends beyond Ethereum's foundational principles, becoming crucial in the context of AI integration. As AI agents begin handling wallet operations and automated transactions, the risk increases that users may cede control over their digital assets, privacy, and intentions to centralized AI service providers. A "CROPS AI" would therefore emphasize local execution where possible, privacy-preserving remote model calls (e.g., using zero-knowledge proofs), and transparent, verifiable processes to maintain user agency. Vitalik highlights a significant convergence between "CROPS Ethereum access layer" and "CROPS AI." Both address the same fundamental challenge: how users can access powerful services—be it blockchain data via RPCs or AI models—without exposing sensitive information or relinquishing ultimate control. This intersection points toward a future digital entry point that is more private, secure, and user-controlled. Ultimately, CROPS is not merely an abstract ideal but a practical guidepost. It steers development—from protocol resilience and wallet design to AI agent safety—towards a future where users retain self-sovereignty even as digital systems grow more complex and powerful. In an era of accelerating AI adoption, these "slow variables" of censorship resistance, openness, privacy, and security may define Ethereum's enduring value.

marsbit4m ago

From Ethereum to AI's 'CROPS': What Exactly is This Set of 'Slow Variables' That Vitalik Repeatedly Emphasizes?

marsbit4m ago

Zcash Bug Could Have Minted Unlimited ZEC Undetected

A critical vulnerability in Zcash's Orchard shielded pool, discovered by researcher Taylor Hornby on May 29, 2026, could have allowed an attacker to create an unlimited amount of undetectable counterfeit ZEC. The flaw, involving an under-constrained element in the Orchard circuit, existed from the pool's 2022 activation until an emergency fix was deployed by June 2, 2026. Hornby identified the bug using AI-assisted auditing tools and confirmed its exploitability in a test environment. Due to Orchard's privacy features, which hide transaction amounts and history, there is no cryptographic way to prove whether the vulnerability was exploited before the fix. While Shielded Labs assesses prior exploitation as unlikely, this uncertainty has sparked a debate on proving supply integrity in privacy-preserving systems. In response, Shielded Labs and other developers are exploring a network upgrade, potentially involving a new shielded pool and formal verification of the circuit rules to prevent future vulnerabilities and allow verification of the ZEC supply's integrity. ZEC's price fell nearly 45% following the disclosure.

bitcoinist13m ago

Zcash Bug Could Have Minted Unlimited ZEC Undetected

bitcoinist13m ago

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.

marsbit1h ago

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

marsbit1h ago

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.

marsbit1h ago

Token Inefficient, Economy Tokenless

marsbit1h ago

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.

marsbit1h ago

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

marsbit1h ago

Trading

Spot
Futures

Hot Articles

How to Buy SHIB

Welcome to HTX.com! We've made purchasing SHIBA INU (SHIB) simple and convenient. Follow our step-by-step guide to embark on your crypto journey.Step 1: Create Your HTX AccountUse your email or phone number to sign up for a free account on HTX. Experience a hassle-free registration journey and unlock all features.Get My AccountStep 2: Go to Buy Crypto and Choose Your Payment MethodCredit/Debit Card: Use your Visa or Mastercard to buy SHIBA INU (SHIB) instantly.Balance: Use funds from your HTX account balance to trade seamlessly.Third Parties: We've added popular payment methods such as Google Pay and Apple Pay to enhance convenience.P2P: Trade directly with other users on HTX.Over-the-Counter (OTC): We offer tailor-made services and competitive exchange rates for traders.Step 3: Store Your SHIBA INU (SHIB)After purchasing your SHIBA INU (SHIB), store it in your HTX account. Alternatively, you can send it elsewhere via blockchain transfer or use it to trade other cryptocurrencies.Step 4: Trade SHIBA INU (SHIB)Easily trade SHIBA INU (SHIB) on HTX's spot market. Simply access your account, select your trading pair, execute your trades, and monitor in real-time. We offer a user-friendly experience for both beginners and seasoned traders.

11.0k Total ViewsPublished 2024.03.29Updated 2026.06.02

How to Buy SHIB

Discussions

Welcome to the HTX Community. Here, you can stay informed about the latest platform developments and gain access to professional market insights. Users' opinions on the price of SHIB (SHIB) are presented below.

活动图片

Top Questions