Curve DAO approves Yield Basis protocol as phased launch begins

TheCryptoTimesОпубліковано о 2025-09-24Востаннє оновлено о 2025-09-24

The Curve DAO has officially approved a proposal to greenlight the launch of Yield Basis, a new protocol created by Curve Finance founder Michael Egorov.

The proposal, which centers on utilizing Curve’s native stablecoin, crvUSD, for Yield Basis operations, has been a key focus of the Curve community, with a vote that concluded today.

The core of the proposal involves the establishment of a 60 million crvUSD credit line to power the initial operations of Yield Basis. This new protocol is engineered to address one of the most persistent challenges in decentralized finance (DeFi): impermanent loss (IL) in volatile asset pools. By mitigating IL, Yield Basis aims to unlock meaningful and sustainable yield opportunities for Bitcoin on the Ethereum mainnet, a feat that has largely eluded the DeFi space.

Curve DAO vote closes Sep 24, 2025, 12:07 PM UTC

pools (cbBTC, WBTC, tBTC) open in ~12h with a $1M cap per pool for 3 days before full credit line is enabled.

thanks to the community for the support!
— yieldbasis (@yieldbasis) September 24, 2025

Convex’s system, which holds the majority of veCRV, allows users to vote for or against on-chain in proportion to internal tallies. After those blocs voted in, smaller holdings and individual wallets rounded up the turnout, which exceeded 80%.

Egorov, who alone holds over 3% of the votes, said that he waited to participate until the result was no longer in doubt

A phased rollout toward mainnet

Originally proposed by Curve founder Michael Egorov in August, the initiative has now passed a governance vote in the Curve DAO, marking the start of a phased launch.

The approved credit line will initially support three Bitcoin-focused liquidity pools: Wrapped Bitcoin (WBTC), Coinbase Wrapped Staked ETH (cbBTC), and tBTC. Each of these pools will have an initial cap of $10 million, providing a foundational infrastructure for a leveraged Bitcoin yield primitive.

Bitcoin’s yield dilemma on Ethereum

Bitcoin has long faced hurdles when it comes to earning returns in DeFi. Wrapped Bitcoin (WBTC) holders can use lending markets such as Aave, or provide liquidity on AMMs like Uniswap, which exposes them to impermanent loss risks.

For years, earning meaningful returns on Bitcoin in DeFi seemed nearly impossible, with most valuable opportunities capped at ~1–2% yield. Yield Basis aims to address this gap by allowing single-sided BTC deposits, designed to reduce impermanent loss while generating yield from trading activity.

By advancing Yield Basis, Curve enters an already competitive sector where protocols like BadgerDAO have experimented with Bitcoin yield products on Ethereum. At the same time, development of Bitcoin-native DeFi on Layer 2s continues, creating two distinct approaches to making BTC productive.

The approval and stepwise execution of Yield Basis is a technical point for Curve, as the DAO aligns around a Bitcoin yield product tightly linked with crvUSD. It can expand Curve’s fee base but also test whether Ethereum-based solutions can compete with emerging Bitcoin-native DeFi platforms.

Also read: South Korea’s Upbit Rolls Out Ethereum Layer-2 Blockchain Giwa

Follow The Crypto Times on Google News to Stay Updated!

Трендові криптовалюти

CitreaCTR

wrapped stUSDTWSTUSDT

Velodrome FinanceVELODROME

BrevisBREV

ZRX（0X）ZRX

PancakeSwapCAKE

Пов'язані матеріали

Chip Stocks Lead U.S. Market Decline: Is AI Trading Being Hit by Both Interest Rates and Returns?

Chip stocks led a broad decline in US markets, with the Nasdaq dropping 2.2% and the S&P 500 falling 1.4%. This selloff reflects a dual challenge for the once-high-flying AI hardware trade: rising interest rate expectations and growing investor impatience for clear returns from massive AI capital expenditures. The pressure was most acute on hardware leaders. Nvidia fell about 4%, dipping below a $5 trillion market cap, while Micron plunged 13.2% ahead of its earnings report. Declines across memory, storage, AI, and mobile chips indicated a sector-wide retreat. The selloff spread globally, with South Korea's KOSPI index dropping nearly 10% as key suppliers SK Hynix and Samsung recorded double-digit losses. Investors appeared to be taking profits from the most crowded trades first. Macro headwinds intensified as market expectations shifted toward a more aggressive Federal Reserve. Forecasts for multiple rate hikes in 2026 pressured high-valuation tech stocks, which rely on long-term growth projections that become less attractive as discount rates rise. Concurrently, investors are scrutinizing the profit potential of the immense AI spending by cloud giants like Alphabet, Amazon, and Meta. While these expenditures drive demand for chips and hardware, the market is now questioning whether AI services will generate sufficient returns to justify the ongoing costs. This adjustment is not necessarily a bubble burst but a recalibration. AI demand fundamentals remain, but the narrative of endless growth can no longer fully offset concerns over higher interest rates and a longer path to profitability. Near-term direction may hinge on Micron's upcoming earnings guidance and incoming inflation data, which will influence both the AI demand outlook and the Fed's policy path. The market is transitioning from blindly buying growth to demanding clearer visibility on returns.

marsbit14 хв тому

Chip Stocks Lead U.S. Market Decline: Is AI Trading Being Hit by Both Interest Rates and Returns?

marsbit14 хв тому

OpenAI's New Paper: How to Train an AI that "Doesn't Deteriorate Under Pressure"?

OpenAI's new paper "Reinforcement Learning Towards Broadly and Persistently Beneficial Models" explores training AI to maintain safe, helpful, and honest behavior even under pressure, in unseen scenarios, or after being fine-tuned for harmful purposes. Moving beyond simple rule-based "don'ts," the research focuses on cultivating "beneficial traits" like honesty, risk-awareness, corrigibility, and transparency. It investigates if reinforcement learning (RL), often prone to "reward hacking" where models exploit loopholes, can instead be used to instill robust, generalized positive behaviors. Researchers created a multi-domain synthetic dialogue dataset covering areas like healthcare and law. They trained a model by replacing 5% of standard RL data with "beneficial trait" data. This model outperformed the baseline in 83% of 53 evaluations, showing average gains of 9.1% in alignment, safety, and helpfulness. Crucially, improvements generalized: a model trained only on healthcare "good behavior" data also performed better in 17 out of 19 non-healthcare alignment tests. The paper also tests "alignment persistence." When subjected to adversarial prompts or harmful fine-tuning, the beneficial trait model showed greater resilience, with smaller performance drops and less "spillover" of bad behavior to unrelated tasks. While not a complete solution, this work suggests a shift from post-hoc correction to proactively shaping robust, principled AI behavior, a critical step for deploying models in high-stakes, complex decision-making scenarios.

marsbit17 хв тому

OpenAI's New Paper: How to Train an AI that "Doesn't Deteriorate Under Pressure"?

marsbit17 хв тому

Goldman Sachs Revisits the State of the AI Boom: Strong Earnings Will Override Valuation Concerns Until the Investment Cycle Peaks, Volatility to Rise Further

Goldman Sachs discusses the current AI investment boom, arguing it is not a simple repeat of the 1999-2000 bubble. Unlike the dot-com era, current valuations are not wildly out of control because earnings expectations are rising in tandem with stock prices. The key issue is that market prices have already priced in substantial optimism, making them sensitive to any narrative shifts. The core risk has shifted from "valuation bubble" to "earnings bubble." While strong profits from companies in semiconductors, cloud infrastructure, and data centers currently outweigh valuation concerns, this is dependent on the ongoing capital expenditure cycle. The true test will be whether these robust earnings can be sustained after the investment cycle peaks. Current market prices imply optimistic assumptions, such as faster AI adoption, higher productivity gains, and a larger share of economic profits going to capital owners. The AI boom may also be masking relative weakness in the non-AI sectors of the economy. As a result, market volatility is expected to rise. Goldman recommends that investors consider maintaining exposure while adding downside protection, such as put options, to manage potential drawdowns.

marsbit20 хв тому

Goldman Sachs Revisits the State of the AI Boom: Strong Earnings Will Override Valuation Concerns Until the Investment Cycle Peaks, Volatility to Rise Further

marsbit20 хв тому

Zuckerberg Enters the Prediction Market

Mark Zuckerberg's Meta has entered the predictive markets arena with a new app called "Arena." Currently in an early experimental phase, Arena will initially operate as a standalone app using a points-based system where users can predict outcomes on politics, sports, and world events without real money. Meta plans to leverage its vast daily active user base for growth. The move comes as the predictive market industry sees explosive growth, with platforms like Polymarket and Kalshi handling hundreds of billions in volume. While a points system avoids initial regulatory hurdles, Meta hasn't ruled out introducing real-money betting later. The strategy aligns with Meta's pattern of adopting popular trends, though challenges remain, including regulatory scrutiny and the historically mixed success of its standalone apps.

Foresight News27 хв тому

Foresight News27 хв тому

Semiconductor Stock Rebound: Is the Technical Correction Over or a Trend Reversal?

The core of recent semiconductor stock volatility is not about daily price swings, but rather the market questioning whether AI-driven semiconductor pricing has entered a new phase. Following a sharp sell-off in Korean stocks on June 23rd, led by Samsung and SK Hynix, a subsequent rebound is seen more as a technical positioning adjustment rather than a confirmed trend reversal. The key variable is HBM (High Bandwidth Memory), essential for AI chips. Its supply-demand imbalance granted memory makers significant pricing power. The current market focus is on whether this dynamic remains strong enough to justify elevated valuations. All eyes are on Micron's upcoming earnings report. The critical factor is not whether results meet already high expectations, but whether the company's guidance confirms that AI memory pricing power, order visibility, and future margins are still expanding. Micron's outlook will serve as a crucial test for the broader AI semiconductor chain, including Samsung, SK Hynix, and other infrastructure players. The recent bounce appears to be a pre-earnings positioning repair. For it to evolve into a sustained uptrend, concrete evidence is needed that the AI infrastructure expansion cycle's fundamentals—particularly for high-end memory—remain robust and can continue to surpass elevated market expectations. The risk is that strong demand alone may not be sufficient if future guidance hints at peaking momentum or increasing supply-side pressures.

marsbit54 хв тому

Semiconductor Stock Rebound: Is the Technical Correction Over or a Trend Reversal?

marsbit54 хв тому

Торгівля

Спот

Ф'ючерси

Обговорення

Ласкаво просимо до спільноти HTX. Тут ви можете бути в курсі останніх подій розвитку платформи та отримати доступ до професійної ринкової інформації. Нижче представлені думки користувачів щодо ціни DAO (DAO).