$2 Billion Market Cap: Tokenized Treasuries Register Explosive Growth

bitcoinistPublicado a 2024-08-27Actualizado a 2024-08-27

Resumen

The tokenized treasuries market finally assembled a $2 billion market capitalization, which took precisely 151 days instead of 452 days....

The tokenized treasuries market finally assembled a $2 billion market capitalization, which took precisely 151 days instead of 452 days. Heavy institutional investments are at the root of this remarkable growth, with the biggest players being none other than BlackRock.

Growth Fueled By Institutional Powerhouses

All of this is changing, though, with the launch of BlackRock’s USD Institutional Digital Liquidity Fund (BUIDL), which has quickly reached a $503 million market cap—nearly obliterating the rest of the tokenized treasury space.

In four months, it grew to a market cap of around $503 million, the single largest fund under the tokenized treasuries space. With the success this fund has had, it gives some level of confidence to investors; hence, it actually attracted more investor interest in tokenized treasuries.

Source: rwa.xyz

Among other well-known funds growing less impressively are Franklin Templeton’s OnChain US Government Money Fund, FOBXX, and the US Dollar Yield from Ondo Finance, USDY. Each of these has grown, too, but from lower bases of $425 million and $364 million, respectively.

This rise in the value of institutional money has not only driven up the market cap but given immense credibility to tokenized treasuries. Now, investors are more willing to dive into this innovative financial product, where the traditional world of government securities gains from the benefits brought about by blockchain technology.

Total crypto market cap at $2.15 trillion on the daily chart: TradingView.com

The Appeal Of Tokenized Treasuries

Tokenized treasuries offer seamless trading on public blockchains like Ethereum and Solana by digitizing US Treasury assets. This technical breakthrough simplifies it and opens up the US Treasury market to more investors, including foreigners. It has transformed securities into digital tokens, lowering market entry barriers.

One of the biggest benefits that tokenization brings is liquidity. In fact, it is a simple exercise for investors who want to redeem their monies, considering that tokenized securities and assets are traded 24/7—an absolute opposite of the traditional markets. One can also redeem his or her tokens for stablecoins through the use of smart contracts, which makes it easy for the investor to access cash without the long processes typical of traditional finance.

Tokenized treasury analysts predict a bright upshot. Apparently, some forecast that the market may extend to slightly over $3 billion by year-end, since the number of DeFi projects and DAOs showing interest in the asset class continues to rise. In particular, such companies find tokenized treasuries very useful for inclusion in their portfolios, as it will allow them stable and risk-free yields.

But the market has to face headwinds. Macroeconomic factors, like changes in interest rates, could also be very bearish for investor sentiment. If the rise in rates is too big, the allure of such tokenized assets might very easily go away. Added to that would be the regulatory hurdles, given that the integration of traditional finance with blockchain technology is still to a large extent uncharted waters.

Featured image from Conduit Financial, chart from TradingView

Christian Encila

Christian Encila

Christian, a journalist and editor with leadership roles in Philippine and Canadian media, is fueled by his love for writing and cryptocurrency. Off-screen, he's a cook and cinephile who's constantly intrigued by the size of the universe.

Lecturas Relacionadas

Just now, DeepSeek V4 updates with DSpark, improving inference speed by 80%

DeepSeek has updated its DeepSeek V4 model with the DSpark speculative decoding framework, achieving a significant 60-85% speedup in generation for Flash models and 57-78% for Pro models while maintaining the same overall throughput. This engineering-focused update, rather than a core architectural change, introduces DSpark to address latency and throughput bottlenecks in high-concurrency production environments. DSpark combines high-throughput parallel generation with adaptive load-aware verification. Its key innovations include a semi-autoregressive generation architecture to model dependencies within token blocks and a hardware-aware confidence-scheduled verification system. This system uses a confidence head to predict token acceptance probabilities, allowing it to dynamically optimize verification length per request and allocate compute only to tokens with the highest expected payoff. The asynchronous scheduler is designed for real-world deployment, ensuring zero-overhead scheduling and continuous CUDA graph replay while preserving the target model's output distribution. In tests across mathematical reasoning, code generation, and daily dialogue, DSpark outperformed state-of-the-art models like Eagle3 and DFlash, increasing average acceptance length by 26.7%-30.9% and 16.3%-18.4% respectively on Qwen3 target models. DeepSeek also open-sourced DeepSpec, a full-stack codebase for training and evaluating speculative decoding draft models, providing a standardized toolkit that includes data preparation tools, model implementations, training code, and evaluation scripts.

marsbitHace 24 min(s)

Just now, DeepSeek V4 updates with DSpark, improving inference speed by 80%

marsbitHace 24 min(s)

BIT Research: The 2028 Halving Is Not the End, the Real Shake-Up of the Bitcoin Mining Industry Is Just Beginning

The Bitcoin mining industry is undergoing its most complex structural adjustment since inception. Despite Bitcoin's price holding near $61,000 and the network hash rate approaching a record 1 ZH/s, miner profitability is deteriorating. The industry is operating close to its breakeven point, with the 2028 halving expected to accelerate consolidation. The challenges extend beyond the halving's subsidy reduction; the industry's revenue model has yet to successfully transition towards a fee-driven structure. Increasingly, mining companies are evolving from simple Bitcoin producers into infrastructure and energy operators, including providers of AI/HPC computing power. Competition is shifting from pure hash rate expansion to business model upgrades. Economic pressure is evident. The theoretical daily mining revenue at current prices is around $78 million, yet the actual figure is only about $33 million—a 136% gap. Transaction fees remain low at roughly $220k daily, far below historical implied levels. With a current estimated industry-wide breakeven price near $65,000, mining alone is struggling to generate ideal profits. The 2028 halving is projected to push the fundamental production cost floor to approximately $93,289. This will likely accelerate a shift towards consolidation among larger, well-capitalized miners with diversified revenue streams. Competitive advantage will belong to institutionalized players with access to low-cost energy, AI/HPC hosting operations, and stronger balance sheets. In essence, Bitcoin mining is transitioning from a "mining business" to an "infrastructure business." Future profitability and resilience will depend less on block rewards and more on diversified income sources like energy management and computational infrastructure services. For investors, the key question is not the halving itself, but which miners can successfully navigate this business model transformation.

marsbitHace 1 hora(s)

BIT Research: The 2028 Halving Is Not the End, the Real Shake-Up of the Bitcoin Mining Industry Is Just Beginning

marsbitHace 1 hora(s)

This is How God Karpathy Uses Claude?

Andrej Karpathy, a prominent figure in AI, has reportedly joined Anthropic, leading to a noticeable decrease in his open-source contributions and social media activity. A document claiming to be his personal "CLAUDE.md" file—a set of instructions for the Claude AI to follow within a specific codebase—has been circulating online. While its authenticity is unverified, the content aligns closely with Karpathy's publicly shared principles on effective AI-assisted programming. The document outlines key rules for AI coding assistants, emphasizing the importance of reading existing code thoroughly before writing new code to maintain consistency. It advises against over-engineering, advocating for simple, surgical modifications that match the project's existing style. Other guidelines include clarifying assumptions upfront, writing meaningful tests, thoughtful debugging, and carefully considering dependencies. The core message is that these principles help prevent common AI coding failures, such as introducing unnecessary abstractions, style drift, or making invisible architectural decisions. The community has noted that even experts like Karpathy require detailed instructions to guide AI effectively, akin to managing a junior developer. A related GitHub repository, "andrej-karpathy-skills," which encapsulates these ideas, is reported to significantly reduce Claude's code error rate. Ultimately, the advice stresses that the best CLAUDE.md is tailored to one's own tech stack and coding practices.

marsbitHace 1 hora(s)

This is How God Karpathy Uses Claude?

marsbitHace 1 hora(s)

Trading

Spot
活动图片