Dogecoin ETFs Dead In March? Only 2 Days Of Inflows And Less Than $1M – Details

bitcoinistPubblicato 2026-03-26Pubblicato ultima volta 2026-03-26

Introduzione

The Dogecoin ETFs, approved in November 2025, have experienced a significant decline in investor interest by March 2026. According to data from SoSoValue, the ETFs saw only two days of net inflows during the month, totaling less than $1 million. This follows a volatile performance history: a strong start with $2.16 million in inflows in November 2025, a sharp drop to $177,890 in December, and a peak of $4.07 million in January 2026. Total net assets have also fluctuated, falling from a high of $10.15 million in January to $9.51 million at the time of reporting. The funds have seen over a week of zero inflows, and daily trading values remain below $1 million, indicating waning demand.

When the Dogecoin Exchange-Traded Funds (ETFs) were first approved back in November 2025, it came as a welcome development for the community. This put the meme coin in the league with the likes of Bitcoin and Ethereum, as they continue to make waves with their Spot ETFs. The first month of trading had gone as expected, attracting over $2 million in inflow from investors. But with the month of March 2026, things look to be going left for the Dogecoin ETFs.

Dogecoin ETFs Have Seen Only 2 Days Of Inflow So Far

The month of March is almost over, with only about five days left, but so far, Dogecoin ETFs have only seen two days of net inflow, according to data from SoSoValue. The first of these inflows was at the start of the month when around $779,100 flowed into Dogecoin ETFs, pushing its cumulative total inflow so far above $7.6 million for the first time.

After this initial inflow that was recorded on March 2, 2026, the Dogecoin ETFs would go dormant again. In the almost two weeks that followed, there was 0 inflow into the exchange-traded products, while traded values fluctuated wildly, and interest waned.

Then, on March 13, 2026, there was another inflow trend, although lower this time. The value came out to $193,360 in daily inflows, and this brought the total inflows for the month to $972,460. Interestingly, this figure was miles ahead of what was recorded in the previous month of February, with total monthly inflows of $252,530, with only a single day of inflows.

Source: SoSoValue

Since the March 13 inflows, Dogecoin ETFs have gone back to 0 inflows once again, with over a week of no liquidity moving into the funds. Total daily traded values across the funds have also remained below the $1 million mark, while Total Net Assets sit at $9.51 million at the time of this report.

How The ETFs Have Fared So Far

With barely five months of trading, the Dogecoin ETFs have had a rather interesting trajectory. Following the first month of trading that saw monthly net inflows hit $2.16 million in November 2025, the funds would go on to have their worst month so far right after. In December 2025, total net inflows to Dogecoin ETFs came out to only $177,890, and the total net assets dropped from $6.29 million in November to $5.07 million by December.

January 2026 has been the most bullish month so far, with $4.07 million in monthly net inflows, $12.31 million in total traded value, and total net assets hitting $10.15 million. The funds are yet to reclaim the peak set in January, with total net assets falling to $8.39 million in February before rising to $9.32 million in March 2026.

DOGE fails to break above $0.1 | Source: DOGEUSDT on Tradingview.com

Domande pertinenti

QHow many days of net inflow have Dogecoin ETFs seen in March 2026 according to SoSoValue data?

ATwo days of net inflow.

QWhat was the total value of inflows into Dogecoin ETFs for the month of March 2026?

A$972,460.

QWhich month was the most bullish for Dogecoin ETFs in terms of monthly net inflows?

AJanuary 2026, with $4.07 million in monthly net inflows.

QWhat was the total net assets of Dogecoin ETFs at the time of the report?

A$9.51 million.

QHow did the inflows in February 2026 compare to those in March 2026?

AMarch's inflows of $972,460 were significantly higher than February's total monthly inflows of $252,530.

Letture associate

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.

marsbit4 min fa

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

marsbit4 min fa

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.

marsbit4 min fa

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

marsbit4 min fa

Trading

Spot
活动图片