Cardano Founder Warns XRP Investors, Is Ripple Doing Something Wrong?

bitcoinistОпубліковано о 2026-04-20Востаннє оновлено о 2026-04-20

Анотація

Cardano founder Charles Hoskinson has warned XRP investors, claiming Ripple is dumping its XRP holdings to fund business acquisitions without benefiting token holders. He alleges Ripple controls 80% of the supply and uses sales to build Web 2.5 companies, with no buybacks or value distribution to XRP investors. Hoskinson also notes the lack of staking or DeFi mechanisms on XRPL reduces organic demand. Despite Ripple CEO Brad Garlinghouse calling XRP central to their vision, Hoskinson compares Ripple to Tether in accruing value solely for itself. XRP price fell nearly 2% to around $1.40.

Cardano founder Charles Hoskinson has warned XRP investors about Ripple, stating that the company is dumping XRP to fund its business operations. He also noted that Ripple’s business doesn’t in any way benefit these XRP holders but only the company’s shareholders.

Cardano Founder Warns XRP Investors About Ripple

In an interview, the Cardano founder stated that there is nothing in the Ripple network that creates buy demand for the XRP token. He further remarked that the company sells its XRP holdings to fund more acquisitions. This came as Hoskinson had alleged that the company allocated up to 80% of the XRP supply to itself.

The Cardano founder also alleged that Ripple’s goal is to inflate the XRP price and then sell their holdings to buy more assets. He noted that Ripple uses the XRP Ledger (XRPL) to run its operations, but there isn’t much demand for XRP, especially since there is no native staking or other DeFi mechanisms on the network.

As such, he believes that Ripple is the only one gaining from holding XRP, describing it as a huge value transfer to just one company while XRP investors do not benefit. The Cardano founder further explained that Ripple is strategically using its XRP holdings to build Web 2.5 companies and that none of the value from these companies has to accrue to XRP.

It is worth noting that Ripple acquired Hidden Road and GTreasury, which have now become Ripple Prime and Ripple Treasury. At the start of the year, Ripple CEO Brad Garlinghouse had assured XRP investors that XRP remains central to their vision of being the internet of value. He has also, on several occasions this year, described XRP as the ‘North Star’ of their operations.

No Commitment On Ripple’s End To The XRP Ecosystem

The Cardano founder indicated that there was no commitment on Ripple’s part to XRP investors, despite its large holdings and its use of the token to fund acquisitions. He noted that the company doesn’t conduct any XRP buybacks, even when it generates revenue or profits. Instead, they only continue to sell more XRP.

Hoskinson also mentioned that XRP investors do not have any rights in Ripple or any access to stock options simply by being XRP holders. Interestingly, he likened Ripple to Tether, noting how these companies accrue all the value without their users or XRP investors, in this case, getting anything. However, it is worth noting that Ripple has continued to integrate XRP into its platforms, most recently with the launch of native XRP capabilities on Ripple Treasury.

At the time of writing, the XRP price is trading at around $1.40, down almost 2% in the last 24 hours, according to data from CoinMarketCap.

ADA trading at $0.24 on the 1D chart | Source: ADAUSDT on Tradingview.com

Пов'язані питання

QWhat did Cardano founder Charles Hoskinson warn XRP investors about regarding Ripple?

ACharles Hoskinson warned that Ripple is dumping XRP to fund its business operations and that this benefits only Ripple's shareholders, not XRP holders.

QAccording to Hoskinson, how does Ripple use its XRP holdings to support its business strategy?

AHoskinson alleged that Ripple sells its XRP holdings to fund acquisitions and build Web 2.5 companies, with the goal of inflating XRP's price to sell their holdings for more assets.

QWhat did Hoskinson claim about the demand for XRP on the XRP Ledger (XRPL)?

AHe stated there isn't much demand for XRP on the XRP Ledger, especially because it lacks native staking or other DeFi mechanisms.

QHow did Hoskinson describe the relationship between XRP investors and Ripple's financial benefits?

AHe described it as a huge value transfer solely to Ripple, with no benefits accruing to XRP investors, who have no rights in Ripple or access to stock options.

QWhat recent integration did Ripple make involving XRP, as mentioned in the article?

ARipple recently integrated native XRP capabilities into Ripple Treasury, following acquisitions like Hidden Road and GTreasury.

Пов'язані матеріали

It Took Me a Year to See the Bitter Truth About Agent Payments

After a year building infrastructure for the Agent economy, engaging with major players like Stripe, Visa, and Coinbase, the author shares a sobering analysis of the current state of Agent payments. The core finding is a stark lack of genuine, immediate demand across most envisioned use cases. The article breaks down four key market segments: 1. **Agent-to-Merchant (Consumer Shopping):** For most product categories (e.g., clothing, electronics), conversational AI shopping is a step backwards from visual e-commerce interfaces. While agents excel at understanding needs, they can't replace side-by-side product comparison. Real merchant interest is defensive "Agent Engine Optimization," not driven by current customer demand. Potential exists for high-frequency, low-decision purchases (like food delivery) or navigating complex store UIs, but these require massive B2C distribution channels dominated by giants like Amazon. 2. **Agent-to-API (Developer Services):** Developers already have subscriptions and billing relationships for APIs (compute, data). Prepaid balances solve micro-payment issues for low transaction volumes. A deeper structural problem is that major SaaS vendors' business models rely on enterprise contracts, resisting granular pay-per-call pricing. While protocols like MPP and x402 serve the long tail of niche services, this market is small and developers are historically low-willingness-to-pay. 3. **Agent-to-Agent:** This remains largely theoretical with minimal transaction volume. While it represents a long-term bet on a fundamentally new transaction infrastructure (sub-second, micro-penny to million-dollar, multi-party settlements), it does not constitute a present market. 4. **Agent-to-Finance:** This is the only category with existing, paying demand. Integrating AI into financial workflows (trading, portfolio management) is a natural evolution and enables new capabilities like autonomous rebalancing. However, competition favors established, regulated institutions. The "real problem" is not moving money between agents, but the broader challenge of **coordination**—orchestrating work between agents and humans, verifying outcomes, and settling results. Payment is just one component of settlement, which is itself part of coordination. Companies that solve the coordination layer will subsume payment, not the other way around. While well-funded incumbents build defensively for a long-term future, startups must find where the market is today—which, for the author's team, lies outside these four categories in an area of real, growing, and underserved activity.

marsbit2 хв тому

It Took Me a Year to See the Bitter Truth About Agent Payments

marsbit2 хв тому

It Took Me a Year to See the Hard Truth About Agent Payments

**Title: It Took Me a Year to See the Hard Truth About Agent Payments** Over the past year, I've worked on infrastructure for the Agent economy, engaging with major players like Stripe, Visa, Coinbase, and numerous startups. The findings reveal a stark reality: genuine, widespread demand for Agent-based payments does not yet exist. **Key Observations:** * **Agent-to-Merchant (Shopping):** The user experience for AI shopping often falls short, especially for visual product discovery. While AI excels at understanding needs, conversational interfaces can't yet replace browsing and comparing multiple products visually. Current merchant interest is largely defensive ("Agent Engine Optimization") for a future that hasn't arrived. High-frequency, low-friction purchases (like food delivery) are potential fits, but lack open APIs and face high AI inference costs. Simpler, more affordable, or cross-language interactions for complex UIs are a niche opportunity but require massive consumer distribution to scale. * **Agent-to-API (Developer Tools):** Developer payment needs for APIs (computing, data, models) are already met through subscriptions and prepaid credits. The core challenge is not payment friction but supplier economics: most large SaaS providers prefer enterprise contracts over micropayments for API calls. Protocols like MPP and x402 suit the long-tail of smaller services but cater to a developer market historically reluctant to pay for these tools. Major infrastructure needs at the top of the stack are already being addressed. * **Agent-to-Agent (Machine Commerce):** This is a long-term vision with almost no current transaction volume. While a future with high-speed, high-frequency, multi-party machine-to-machine transactions would require novel infrastructure, it remains theoretical. The market is not here yet. * **Agent-to-Finance:** This is the only category with clear, present demand. Financial professionals and DeFi users already pay for tools, and AI augmentation is a natural evolution. Autonomous AI agents can enable entirely new financial strategies. However, competition is fierce from established, regulated incumbents who can more easily layer AI onto their existing products. **The Core Insight:** Companies, especially giants with long time horizons, are building defensively for a potential future of mass machine commerce. For them, early investment is a low-cost hedge. For startups, the current market reality is different. The primary challenge isn't just moving money between agents (payments). The larger, unsolved problem is **orchestration** – coordinating work between agents and humans, verifying outcomes, and then settling. Payment is just a part of settlement, which is just a part of orchestration. Companies that solve the orchestration problem will subsume payments, not the other way around. After a year of building, we see the real, growing, and underserved market opportunity lies in this broader domain of orchestration.

链捕手25 хв тому

Claude Opus 4.8 Finds a $4.5 Billion Bug: The AI Era is Mass-Producing Hackers

A researcher discovered a critical "infinite mint" vulnerability in the Zcash cryptocurrency's Orchard protocol using Claude Opus 4.8, leading to a swift fix but also a 50% market drop, erasing billions in value. This incident highlights a new era where powerful, accessible AI models are dramatically lowering the barrier to finding software vulnerabilities. Previously, the security community feared specialized models like Claude Mythos Preview, capable of finding decades-old zero-day exploits. The Zcash case, however, involved a publicly available, general-purpose model. This shift makes advanced security auditing—and attack capabilities—accessible to far more people, not just experts. The mass democratization of vulnerability discovery brings a dual challenge: a flood of low-quality, AI-generated false reports that overwhelm maintainers, and the real, rapid uncovering of deep, dangerous bugs. Open-source projects, often understaffed and unfunded, are particularly vulnerable to this "attention DDoS." The article cites examples like curl shutting down its bug bounty program due to the unsustainable workload. Our perceived digital safety has often been luck, relying on the high cost and effort required to find deeply hidden flaws in complex systems, as seen with historical vulnerabilities like Heartbleed or Baron Samedit. AI changes this cost structure, effectively "mass-producing flashlights" to illuminate every corner of our codebase. While large companies operate extensive security chains involving external white-hat hackers and massive defensive operations, the global cybersecurity workforce faces a severe shortage, especially of experienced personnel capable of analyzing complex threats and coordinating fixes. The core dilemma emerges: AI makes *finding* bugs cheap and scalable, but *fixing* them remains a slow, expensive, and human-intensive process. The article concludes that AI won't destroy the internet but acts as a bright light, revealing that our digital existence is not inherently secure but is precariously maintained by ongoing human effort. The true cost in the AI era may not be discovery, but whether there will be enough people left willing and able to do the hard work of repair.

marsbit58 хв тому

Claude Opus 4.8 Finds a $4.5 Billion Bug: The AI Era is Mass-Producing Hackers

marsbit58 хв тому

Ethereum Price Prediction: ETH Could Double While Cardano (ADA) and This Newcomer Token Near 500% Rally

As the cryptocurrency market anticipates a potential bull cycle, investor focus is shifting toward projects with high return potential. Ethereum (ETH), currently at approximately $2,014, is highlighted for its long-term value, with predictions suggesting a possible 2x increase to over $4,000. Cardano (ADA), trading around $0.2329, remains a choice for its research-driven development and scalability focus, though some seek higher upside elsewhere. Significant attention is drawn to Little Pepe (LILPEPE), a new Layer 2 Ethereum-compatible token priced at $0.0022 in its presale stage, which has raised over $28.19 million. The project aims to build a dedicated blockchain for meme communities, offering lower costs and faster transactions. Backers speculate a potential 500% gain, driven by strong presale demand, planned ecosystem features like staking and a launchpad, and active community engagement through giveaways. While Ethereum and Cardano are viewed as foundational investments, LILPEPE represents a newer, high-growth opportunity for investors targeting the meme coin sector.

TheNewsCrypto1 год тому

Ethereum Price Prediction: ETH Could Double While Cardano (ADA) and This Newcomer Token Near 500% Rally

TheNewsCrypto1 год тому

Codex Goal Mode Usage Guide: How to Make AI Continuously Pursue a Specific Objective

"Codex Goal Mode: How to Make AI Work Continuously Toward a Specific Goal" OpenAI's Codex "goal mode" (/goal) transforms the AI from a reactive code assistant into a proactive execution agent capable of working autonomously for hours or even days to achieve a defined objective. To maximize its effectiveness, follow these key principles: 1. **Define Clear, Verifiable Exit Criteria:** The goal prompt should be a concise, measurable success condition, not a lengthy specification. Use quantifiable metrics like "reduce build time by 30%" or "achieve 100% test parity." 2. **Provide Initial Guidance and Tools:** Direct Codex toward likely problem areas and specify available tools (e.g., browsers, testing environments) to prevent it from exploring unproductive paths. 3. **Enable Progress Measurement:** Equip Codex with ways to track advancement, such as creating comparison tools for visual tasks or evaluation sets, ensuring it can gauge its own progress. 4. **Use a Realistic Execution Environment:** For tasks like performance optimization, provide access to environments that closely mimic production (e.g., similar configs, databases) to yield valid results. 5. **Be Cautious with Visual Goals:** Avoid vague "pixel-perfect" instructions. Instead, supplement visual references with functional checklists or design system specifications to prevent Codex from obsessing over minor details. 6. **Implement Progress Tracking:** For long-running tasks, have Codex commit code to draft PRs, update progress documents, or send Slack updates to maintain visibility into its work. 7. **Review and Consolidate Results:** Once the goal is met, instruct Codex to review its work, clean up ineffective experimental code, and reflect on what strategies succeeded or failed. Ultimately, using goal mode shifts the developer's role from writing prompts to managing a persistent engineering agent—defining objectives, establishing metrics, configuring environments, and conducting final reviews.

marsbit2 год тому

Codex Goal Mode Usage Guide: How to Make AI Continuously Pursue a Specific Objective

marsbit2 год 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 ADA (ADA).