Dogecoin And Shiba Inu May Be Gearing Up For Another Rally After This Happened

bitcoinist2026-03-20 tarihinde yayınlandı2026-03-20 tarihinde güncellendi

Özet

US financial regulators (SEC and CFTC) have jointly issued guidance classifying digital assets, explicitly naming Dogecoin and Shiba Inu as digital commodities — the same category as Bitcoin and Ethereum. This removes them from being considered securities, providing regulatory clarity. The classification means DOGE and SHIB are now viewed as assets deriving value from blockchain ecosystems and market dynamics, not as investment contracts. While the immediate market reaction was muted, this status could pave the way for potential Spot ETFs, similar to Bitcoin. Grayscale has already indicated SHIB qualifies for a spot ETF under current standards.

US financial regulators have issued a clarification on how federal securities laws apply to crypto assets, and Dogecoin and Shiba Inu are among the direct beneficiaries. The joint guidance, which was published by the SEC and CFTC, formally established five categories for digital assets and explicitly named both meme coins as digital commodities, placing them in the same regulatory class as Bitcoin, Ethereum, and XRP.

Dogecoin And Shiba Inu Officially Classified As Digital Commodities

An interesting decision from US regulators is now setting the stage for a possible turnaround in the price of meme coins like Dogecoin and Shiba Inu. For the first time ever, this clarification directly names the leading names of meme cryptocurrencies (Dogecoin and Shiba Inu) as digital commodities, removing them from the security debate that has weighed on the crypto industry for years.

The joint interpretive release by the SEC and the CFTC finally ended more than a decade of jurisdictional dispute between the two US regulators over how to classify digital assets. According to the release, crypto assets are now divided into five categories: digital commodities, digital collectibles, digital tools, stablecoins, and digital securities.

The first four carry no securities designation by default, while digital securities, which are essentially tokenized versions of traditional financial instruments such as stocks and bonds, are still subject to federal securities laws.

On the other hand, digital commodities are assets whose value derives from a functioning blockchain ecosystem and supply-and-demand dynamics, with decentralization also an important criterion. Both Dogecoin and Shiba Inu were placed in this category alongside Bitcoin, Ethereum, XRP, and Cardano, among others.

SEC Chair Paul Atkins stated that the guidance was designed to provide regulatory clarity “in clear terms” and confirmed that blockchain network activities such as mining, on-chain staking, and protocol airdrops do not automatically qualify as securities offerings.

What The Classification Means For DOGE And SHIB Specifically

The market’s reaction so far has been somewhat muted. Price data show that crypto prices did not surge immediately even after the guidance was released. However, the importance of being classified as a commodity cannot be overstated for Dogecoin and Shiba Inu, considering the fact that these two started as a meme. A February 2025 clarification from the SEC’s Division of Corporation Finance had indicated that meme coins were not securities, but that guidance stopped short of a formal classification.

Both Dogecoin and Shiba Inu have spent recent months moving sideways or struggling to break above resistance levels in terms of price action. However, this might change very soon. Commodity status equates Dogecoin and Shiba Inu with the same regulations backing Bitcoin and Ethereum Spot ETFs in the United States. Spot Dogecoin ETFs are already live and Shiba Inu might be next. Interestingly, Grayscale Investments has already indicated that SHIB qualifies for a spot ETF under the SEC’s Generic Listing Standards framework.

DOGE price stages another recovery attempt | Source: DOGEUSDT on Tradingview.com

İlgili Sorular

QWhat are the five categories of digital assets established by the joint guidance from the SEC and CFTC?

AThe five categories are digital commodities, digital collectibles, digital tools, stablecoins, and digital securities.

QWhich meme coins were explicitly named as digital commodities in the regulatory clarification?

ADogecoin and Shiba Inu were explicitly named as digital commodities.

QWhat is the significance of being classified as a digital commodity for assets like Dogecoin and Shiba Inu?

ABeing classified as a digital commodity removes them from the securities debate, places them in the same regulatory class as Bitcoin and Ethereum, and opens the door for potential products like spot ETFs.

QAccording to the guidance, do activities like mining and airdrops automatically qualify as securities offerings?

ANo, the guidance confirms that blockchain network activities such as mining, on-chain staking, and protocol airdrops do not automatically qualify as securities offerings.

QWhich investment firm has indicated that Shiba Inu qualifies for a spot ETF under the SEC's framework?

AGrayscale Investments has indicated that SHIB qualifies for a spot ETF under the SEC’s Generic Listing Standards framework.

İlgili Okumalar

OpenAI Post-Training Engineer Weng Jiayi Proposes a New Paradigm Hypothesis for Agentic AI

OpenAI engineer Weng Jiayi's "Heuristic Learning" experiments propose a new paradigm for Agentic AI, suggesting that intelligent agents can improve not just by training neural networks, but also by autonomously writing and refining code based on environmental feedback. In the experiment, a coding agent (powered by Codex) was tasked with developing and maintaining a programmatic strategy for the Atari game Breakout. Starting from a basic prompt, the agent iteratively wrote code, ran the game, analyzed logs and video replays to identify failures, and then modified the code. Through this engineering loop of "code-run-debug-update," it evolved a pure Python heuristic strategy that achieved a perfect score of 864 in Breakout and performed competitively with deep reinforcement learning (RL) algorithms in MuJoCo control tasks like Ant and HalfCheetah. This approach, termed Heuristic Learning (HL), contrasts with Deep RL. In HL, experience is captured in readable, modifiable code, tests, logs, and configurations—a software system—rather than being encoded solely into opaque neural network weights. This offers potential advantages in explainability, auditability for safety-critical applications, easier integration of regression tests to combat catastrophic forgetting, and more efficient sample use in early learning stages, as demonstrated in broader tests on 57 Atari games. However, the blog acknowledges clear limitations. Programmatic strategies struggle with tasks requiring long-horizon planning or complex perception (e.g., Montezuma's Revenge), areas where neural networks excel. The future vision is a hybrid architecture: specialized neural networks for fast perception (System 1), HL systems for rules, safety, and local recovery (also System 1), and LLM agents providing high-level feedback and learning from the HL system's data (System 2). The core proposition is that in the era of capable coding agents, a significant portion of an AI's learned experience could be maintained as an auditable, evolving software system.

marsbit44 dk önce

OpenAI Post-Training Engineer Weng Jiayi Proposes a New Paradigm Hypothesis for Agentic AI

marsbit44 dk önce

Your Claude Will Dream Tonight, Don't Disturb It

This article explores the recent phenomenon of AI companies increasingly using anthropomorphic language—like "thinking," "memory," "hallucination," and now "dreaming"—to describe machine learning processes. Focusing on Anthropic's newly announced "Dreaming" feature for its Claude Agent platform, the piece explains that this function is essentially an automated, offline batch processing of an agent's operational logs. It analyzes past task sessions to identify patterns, optimize future actions, and consolidate learnings into a persistent memory system, akin to a form of reinforcement learning and self-correction. The article draws parallels to similar features in other AI agent systems like Hermes Agent and OpenClaw, which also implement mechanisms for reviewing historical data, extracting reusable "skills," and strengthening long-term memory. It notes a key difference from human dreaming: these AI "dreams" still consume computational resources and user tokens. Further context is provided by discussing the technical challenges of managing AI "memory" or context, highlighting the computational expense of large context windows and innovations like Subquadratic's new model claiming drastically longer contexts. The core critique argues that this strategic use of human-centric vocabulary does more than market products; it subtly reshapes user perception. By framing algorithms with terms associated with consciousness, companies blur the line between tool and autonomous entity. This linguistic shift can influence user expectations, tolerance for errors, and even perceptions of responsibility when systems fail, potentially diverting scrutiny from the companies and engineers behind the technology. The article concludes by speculating that terms like "daydreaming" for predictive task simulation might be next, continuing this trend of embedding the idea of an "inner life" into computational processes.

marsbit46 dk önce

Your Claude Will Dream Tonight, Don't Disturb It

marsbit46 dk önce

İşlemler

Spot
Futures
活动图片