Ondo Perps: Bringing Wall Street Prime Brokerage On-Chain?

marsbitPubblicato 2026-04-10Pubblicato ultima volta 2026-04-10

Introduzione

Ondo Perps aims to bring traditional prime brokerage services on-chain by addressing key limitations in existing DeFi and tokenized stock markets. While crypto-native assets like BTC and ETH have mature derivatives markets, real-world assets (RWA), such as stocks, struggle due to structural flaws: over-reliance on stablecoin collateral, isolated liquidity pools, and inefficient capital utilization. Ondo introduces three innovations: allowing tokenized stocks as direct collateral, implementing cross-asset margin systems similar to traditional portfolio margining, and leveraging off-chain liquidity from traditional exchanges like Nasdaq/NYSE instead of building fragmented on-chain markets. This transforms stocks from static assets into active collateral, improves capital efficiency, and enables unified risk management across asset classes. The platform essentially functions as a multi-asset financial account system, blending decentralized and traditional finance. If successful, it could redefine asset boundaries, enhance institutional participation, and create a more integrated financial ecosystem. However, risks remain around liquidity reliability, complex cross-asset清算, and regulatory uncertainty for tokenized securities. The core question Ondo raises is whether traditional distinctions between "money" and "assets" remain relevant when diverse assets can mutually collateralize and interact in a unified market.

Author: 137Labs

I. The Starting Point: Why Have Stock Perps Always Underperformed?

If we look back at the development path of DeFi over the past few years, a clear divergence emerges: the derivatives market for crypto-native assets (BTC, ETH) has matured considerably, while derivatives related to real-world assets (RWA) have remained in the "experimental phase."

Stock perpetual contracts are a classic case study.

From the demand side, the market is actually very clear: global users want to participate in US stock trading with lower barriers and higher efficiency, while also using tools like leverage and hedging for risk management. But from the supply side, whether it was early synthetic asset protocols (like Synthetix) or later on-chain orderbook or AMM models, none have truly solved the core problems.

These attempts often provided price exposure initially but struggled to sustain continuous trading, eventually falling into a cycle of drying liquidity, widening slippage, and user attrition.

Meanwhile, tokenized stocks are rapidly developing as another path. According to mainstream media reports, these assets already offer advantages like 24/7 trading and instant settlement, but their market size remains limited, and they function more as "holding tools" rather than being part of a complete financial system.

Therefore, the key issue is not "whether people want to trade stocks," but rather:

Why can't these assets form a self-sustaining, continuously expanding market structure?

In other words, what's truly missing is not the product, but the underlying mechanisms that allow these products to operate.

II. The Current State: Structural Flaws in DeFi and Tokenized Stocks

If we further deconstruct the existing system, the problems concentrate on two levels: collateral structure and liquidity structure.

First, in the DeFi derivatives system, collateral is highly singular. Mainstream protocols almost exclusively rely on stablecoins as margin. This means all trading activity must be completed using stablecoins as the intermediary. If users hold other assets, be it ETH or tokenized stocks, they must first convert them into stablecoins to participate in derivatives trading.

This design was reasonable in the early days due to the price stability and liquidation convenience of stablecoins. But as asset types increased, it gradually became a structural constraint. Direct relationships cannot be established between assets, causing the entire system to exhibit an "isolated island" characteristic.

Second, while tokenized stocks have made progress in asset representation, their financial functions remain extremely narrow. They can be held, transferred, and even used for simple lending in some scenarios, but they lack more complex uses, such as serving as efficient collateral for derivatives trading or playing a role in multi-asset portfolios.

The deeper issue lies in liquidity. Most tokenized stock projects attempt to "rebuild a market" on-chain, providing trading depth through AMMs or synthetic order books. However, this method is inherently limited by the scale of on-chain capital and cannot compete with the liquidity of traditional exchanges, leading to price deviations, slippage, and transaction cost issues.

Therefore, the core flaws of the current system can be summarized as:

Assets are tokenized, but they cannot form effective financial relationships with each other, and the market also lacks sufficient liquidity support.

III. What Ondo Perps Does: A Three-Part Structural Innovation

Against this backdrop, the emergence of Ondo Perps is not simply providing a new trading platform, but an attempt to simultaneously restructure collateral logic, asset relationships, and liquidity sources.

First, it introduces a key change: allowing tokenized stocks to be used directly as margin. This change might seem like just a parameter adjustment, but it actually alters the entire system's capital flow. Users no longer need to liquidate assets into stablecoins; they can directly use their existing holdings for leverage or hedging operations.

This mechanism brings not only efficiency gains but, more importantly, a change in asset properties. Stocks are no longer just "yield assets" but become a "credit base" that can support other risk exposures. In financial terms, this means assets begin to possess "collateral attributes."

Second, Ondo introduces the concept of cross-asset margin. Traditional DeFi protocols typically use an isolated margin model, where each position's risk is calculated independently. Ondo, however, treats the entire asset portfolio as a whole. This design is closer to portfolio margin in traditional finance, allowing different assets to hedge and support each other.

The change behind this is structural: risk is no longer calculated per single asset but per portfolio. The resulting outcome is significantly improved capital utilization, alongside the introduction of more complex risk transmission paths.

Third, and most crucially, is the change in the liquidity model. Ondo does not attempt to build liquidity on-chain from scratch. Instead, through the issuance and redemption mechanism of tokenized stocks, it connects the on-chain market to traditional exchanges. This means price discovery and liquidity depth can be inherited directly from Nasdaq and NYSE, rather than relying on limited on-chain capital pools.

If this mechanism can operate stably, on-chain trading would no longer be constrained by TVL but could tap into a market worth trillions of dollars.

IV. The Essence: What Is It Actually Doing?

From a higher level, the significance of Ondo Perps lies not in "improving the trading experience" but in redefining the basic structure of the financial system.

Traditional DeFi is more like a "collection of trading tools" where users can switch between different protocols to complete operations like lending, trading, and staking. But these operations are independent of each other, lacking a unified risk management and asset perspective.

Ondo's direction is closer to the prime brokerage system in traditional finance. In this system, users are not operating individual products but managing a complete balance sheet. All assets and liabilities are incorporated into a unified risk framework and dynamically adjusted through portfolio margin.

Therefore, Ondo can be understood as a combination of three functions:

  • A multi-asset collateral system

  • A portfolio risk management engine

  • A clearing layer connecting on-chain and traditional markets

From this perspective, it resembles a "financial account system" more than a single trading platform.

V. Why This Matters: Three Tiers of Impact

If this model can be successfully implemented, its impact will extend beyond a single protocol and could potentially change the entire development path of DeFi.

First is the improvement in capital efficiency. Assets can participate in various financial activities without conversion, reducing intermediate steps and transaction costs while increasing capital turnover speed. This difference is further amplified in high-frequency trading and hedging scenarios.

Second is the dissolution of asset boundaries. In the past, assets like crypto, stocks, and bonds belonged to different systems. In the Ondo model, they can coexist and interact within the same account. This fusion will make asset allocation more flexible and may also give rise to new strategies and products.

Third is the change in user structure. As system complexity increases, average users might find it difficult to fully utilize these functions, while institutional investors and professional traders will become the main participants. This means DeFi is gradually evolving towards "institutionalization," and its market behavior will more closely resemble traditional finance.

VI. Risks and Uncertainties: The More Complex the Structure, the More Hidden the Risks

Despite the promising prospects, this model also introduces new risk dimensions.

The core uncertainty remains liquidity. If the on-chain market cannot stably access the liquidity of traditional exchanges, all mechanisms based on it will be affected, and price deviations and liquidation risks will quickly amplify.

Second is the complexity of the liquidation mechanism. In a multi-asset, cross-market environment, risk transmission paths become more complex. A fluctuation in one asset's price could affect another asset through collateral relationships, triggering a chain reaction. This kind of systemic risk has not been fully validated in DeFi.

Finally, there are regulatory issues. Tokenized stocks involve securities attributes, and their compliance varies across different jurisdictions. If the regulatory environment changes, it could directly impact the sustainability of asset issuance and trading.

Conclusion: A Paradigm Upgrade, or Complex Packaging?

Overall, the core of Ondo Perps is not launching a new type of derivative, but an attempt to build a new financial structure where assets can mutually support each other, price each other, and be cleared within a unified system.

The success of this attempt depends on two key factors: whether liquidity can truly connect to real-world markets, and whether the risk system can remain stable in a complex environment.

Therefore, a relatively clear judgment can be made:

If the liquidity model holds, and risk control can withstand the test of market volatility, then Ondo has the potential to become an important part of on-chain financial infrastructure; conversely, if these prerequisites cannot be met, it might ultimately remain a derivatives platform with more complex functions but a similar essence.

From a more macro perspective, the significance of this attempt might lie in the fact that it raises a more fundamental question:

When different types of assets can all serve as collateral for each other and participate in a unified market, does the traditional boundary between "money" and "assets" still exist?

This is perhaps the proposition that Ondo truly touches upon.

Domande pertinenti

QWhat is the core problem with existing DeFi derivatives for real-world assets (RWA) like stock perps, according to the article?

AThe core problem is not a lack of demand, but the inability to form a self-sustaining, expanding market structure. Existing solutions suffer from a highly singular collateral structure (reliant solely on stablecoins), a lack of effective financial relationships between tokenized assets, and insufficient on-chain liquidity that cannot compete with traditional exchanges.

QWhat are the three key structural innovations introduced by Ondo Perps?

AOndo Perps introduces three structural innovations: 1) Allowing tokenized stocks to be used directly as margin (collateral). 2) Implementing a cross-asset margin (portfolio margin) system that treats a user's entire portfolio as a single entity for risk calculation. 3) Changing the liquidity model by connecting the on-chain market to traditional exchanges (like Nasdaq and NYSE) through the minting and redemption mechanisms of tokenized stocks, rather than building liquidity from scratch on-chain.

QHow does the article describe the fundamental nature of Ondo Perps compared to traditional DeFi?

AThe article states that Ondo Perps is not merely a trading tool but is redefining the basic structure of the financial system. It is compared to a traditional prime brokerage system, functioning as a 'financial account system' that combines a multi-asset collateral system, a portfolio risk management engine, and a clearing layer connecting on-chain and traditional markets. This is a shift from DeFi as a collection of independent tools to a unified system for managing a complete balance sheet.

QWhat are the main risks and uncertainties of the Ondo Perps model mentioned in the article?

AThe main risks are: 1) Liquidity uncertainty: The model depends on a stable connection to traditional exchange liquidity; if this fails, price deviations and liquidation risks amplify. 2) Complexity of the liquidation mechanism: In a multi-asset, cross-market environment, risk transmission paths become complex, potentially causing cascading effects and systemic risk. 3) Regulatory issues: The legal status of tokenized stocks varies by jurisdiction, and regulatory changes could impact the sustainability of asset issuance and trading.

QWhat fundamental question does the Ondo model ultimately raise, according to the article's conclusion?

AThe article concludes that the Ondo model raises a more fundamental question: When different types of assets can mutually act as collateral and participate in a unified market, does the traditional boundary between 'money' and 'assets' still exist?

Letture associate

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.

marsbit45 min fa

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

marsbit45 min fa

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.

marsbit49 min fa

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

marsbit49 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.

marsbit49 min fa

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

marsbit49 min fa

Trading

Spot
活动图片