Tether may tokenize equity to ensure liquidity for investors: Report

cointelegraphPublicado a 2025-12-12Actualizado a 2025-12-12

Resumen

Tether, the issuer of USDT, is reportedly considering tokenizing equity and share buybacks to provide liquidity for investors as it aims for a $500 billion valuation. The company halted a shareholder’s attempt to sell a $1 billion stake, which would have valued Tether at $280 billion. Instead, Tether plans to offer liquidity solutions through tokenization or buybacks after closing a funding round aimed at raising $20 billion for a 3% stake. Tokenized equity enhances liquidity by enabling easier transfers, fractional ownership, and use as collateral in DeFi. The move aligns with growing regulatory support for onchain finance, including the SEC’s recent approval for DTCC to tokenize traditional assets like stocks and bonds.

Tether, the stablecoin company that issues the USDt (USDT) dollar-pegged token, is considering tokenizing investor equity and share buybacks to offer liquidity for investors as it seeks a $500 billion valuation.

Bloomberg reported on Friday, citing a source familiar with the matter, that Tether recently stopped an existing shareholder from selling their stake as the company is in talks to raise $20 billion for a 3% stake in the stablecoin's issuer business.

The investor sought to sell a $1 billion stake that valued Tether at $280 billion, Bloomberg reported. In response, Tether plans to offer investor liquidity through tokenization or share buybacks after the funding round closes.

Cointelegraph reached out to Tether but had not received a response by the time of publication.

Tokenizing a company’s equity can increase liquidity by making shares easier to transfer, fractionalize and borrow against. Onchain equity allows holders to maintain their positions while using a tokenized representation of their equity as collateral in decentralized finance (DeFi) applications.

The differences between tokenized equity and shares issued through the traditional financial system. Source: Cointelegraph

Related: Tether solvency fears are ‘misplaced’ as company sits on large surplus: CoinShares

Tokenized finance is gaining steam as US regulators move to overhaul legacy financial tech

On Thursday, the US Securities and Exchange Commission (SEC) gave the green light to the Depository Trust and Clearing Corporation (DTCC), a clearinghouse and settlement company, to tokenize stocks, exchange-traded funds and bonds.

“US financial markets are poised to move onchain,” SEC Chair Paul Atkins said on Thursday, adding, “Onchain markets will bring greater predictability, transparency, and efficiency for investors.”

Source: Paul Atkins

Financial services company J.P. Morgan facilitated a $50 million tokenized bond issue for crypto investment company Galaxy Digital Holdings on the same day as Atkins’ announcement.

Crypto exchanges are also looking to expand trading of tokenized products, following the SEC’s nod to the DTCC and Atkins’ comments.

Coinbase, a US-based cryptocurrency exchange, is expected to announce its expansion into tokenized stocks and prediction markets as early as Wednesday.

The company told Cointelegraph that it will host a livestream to showcase new products, but did not specify which products would be unveiled.

Tokenized public stocks are still in the early stages of adoption, with nearly $700 million in public equities tokenized at the time of this writing, according to RWA.xyz data.

Magazine: Bitcoin whale Metaplanet ‘underwater’ but eyeing more BTC: Asia Express

Lecturas Relacionadas

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.

marsbitHace 1 hora(s)

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

marsbitHace 1 hora(s)

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.

marsbitHace 1 hora(s)

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

marsbitHace 1 hora(s)

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.

marsbitHace 1 hora(s)

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

marsbitHace 1 hora(s)

Trading

Spot
活动图片