Ethereum to $2,400? BlackRock’s latest $41.9M buy may be just the start it needs!

ambcryptoPublished on 2026-03-05Last updated on 2026-03-05

Abstract

Ethereum surged above $2,000 in March 2026, driven by strong institutional interest, including a significant $41.9 million purchase by BlackRock. Despite some ETF outflows, institutional confidence appears long-term. Network activity also reached historic highs, with daily active addresses up 82% and new addresses growing by 64%. Trading at $2,075, Ethereum is testing key resistance. Technical indicators like MACD and RSI suggest bullish momentum, with a potential breakout toward $2,400 if resistance is cleared. Strong institutional support and organic growth position Ethereum for a major upward move.

Ethereum is back above $2,000 for the third time in March 2026, powered by a wave of institutional buying.

BlackRock’s sustained backing, along with other institutional moves, has solidified Ethereum’s position despite ongoing market volatility. Ethereum now faces a major wall – Will it break through or falter once again?

BlackRock buys $41.9M in Ethereum, fueling momentum

On 03 March 2026, BlackRock bought $41.9 million worth of Ethereum, giving the market a solid boost.

Despite $10.8 million in short-term ETF outflows, led by Fidelity with $66.7 million in outflows, Grayscale’s ETHE saw $4.7 million in outflows while its Ethereum fund brought in $18.7 million.

BlackRock’s bold move made one thing clear – It isn’t about quick profits. It is about long-term belief in Ethereum’s future.

This is no small move. Institutions have been driving Ethereum’s price, and BlackRock’s actions have made it clear the big players may be in it for the long haul. Their decision to keep buying through market turbulence speaks volumes about their confidence.

Network activity hits historic highs with 82% growth in active addresses

By 04 March, Ethereum’s network activity had surged, with daily active addresses reaching 837.2k – Up 82%. According to Santiment analysts, 284.8k new Ethereum addresses were created daily too – A 64% uptick.

These figures are illustrative of Ethereum’s organic growth and adoption. The network is thriving, supported by real user growth, ensuring a strong future.

Can Ethereum break $2,150 and reach $2,400?

At the time of writing, Ethereum was trading at $2,075, pushing against its local resistance on the price charts.

The 4-hour timeframe chart revealed strong momentum, with an ascending triangle signaling that a breakout may be near. Clear this resistance, and $2,400 would be possible, setting ETH up for a major rally.

The MACD and RSI flashed signs of strong bullish momentum too. The MACD crossover was solid, and the RSI was gaining strength.

Put simply, Ethereum’s price seemed poised to break through resistance and move towards $2,400. However, if it loses the ascending support, there could be downside risk. However, with aggressive institutional buying continuing, that outcome might be unlikely.

With strong institutional support and record network activity, Ethereum is ready for its next big move. The coming headlines will show if it can break free.


Final Summary

  • Ethereum’s price surge has been driven by institutional buying and impressive network activity.
  • If Ethereum clears the $2,150 resistance, $2,400 will be the next target.

Trending Cryptos

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.

marsbit1h ago

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

marsbit1h 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.

marsbit1h ago

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

marsbit1h 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.

marsbit1h ago

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

marsbit1h ago

Trading

Spot

Hot Articles

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 ETH (ETH) are presented below.

活动图片