Garlinghouse Reveals Why Ripple Really Pivoted To Its Own Stablecoin

bitcoinistPublicado em 2026-03-27Última atualização em 2026-03-27

Resumo

Ripple CEO Brad Garlinghouse explained that the company launched its own stablecoin, RLUSD, to internalize a role it was already playing at scale—having previously minted 20% of all USDC due to its large payment flows. The decision was driven by Ripple's substantial balance sheet strength, which positions it to offer a compliant, institutionally-focused stablecoin, especially after observing vulnerabilities like USDC's depeg during the Silicon Valley Bank collapse. Garlinghouse emphasized that trust, licensing, and transparency will differentiate stablecoins as the market matures, and he expects regulatory progress in the U.S. to continue, with further clarity anticipated by the end of May.

Ripple’s decision to launch RLUSD was not a sudden expansion beyond XRP so much as a move to internalize a business it was already helping power at scale. Speaking at FII Priority Miami 2026, Ripple CEO Brad Garlinghouse said the company’s role in stablecoin flows had grown large enough that building its own product became the logical next step.

Why Ripple Entered the Stablecoin Market

Garlinghouse said the turning point came well before RLUSD’s launch 13 months ago. “Two years ago, we were minting 20% of all USDC,” he said, tying that activity directly to Ripple’s payments business. With more than $100 billion in payment flows already processed, Ripple concluded that if it was already a major engine behind stablecoin usage, it made sense to bring that function in-house.

He also linked the decision to a moment of stress in the stablecoin market. Garlinghouse pointed to USDC’s temporary depeg during the Silicon Valley Bank collapse as a reminder that institutional users care about balance-sheet strength as much as blockchain rails.

“Circle came out and said, hey, we’ll stand in the gap. We’ll guarantee the peg. And it didn’t move because at that point, Circle didn’t have a balance sheet,” he said. “Ripple has on our balance sheet, you know, 60, 70 billion dollars of crypto. We have about four billion dollars of US dollars. And so I think we’re in a position to really have a very compliant, very institutional focused stablecoin.”

According to Garlinghouse, stablecoins are increasingly adopted not because companies want exposure to crypto branding, but because they want a better way to solve treasury, settlement and cross-border transfer problems. That broader shift, he argued, is already reshaping how the sector is perceived.

Garlinghouse compared the current state of crypto to the internet industry in the late 1990s, when companies led with the technology rather than the use case. “We don’t talk about anything as an internet company now because it’s just prevalent in the background,” he said. “And I think that’s where some of the blockchain and crypto based solutions are heading”. Companies, he added, “just want to solve a payments problem. They want to solve a custody problem.”

On market structure, Garlinghouse expects the stablecoin field to get more crowded before it gets smaller. He said the biggest banks are already evaluating whether they should issue their own stablecoins, but questioned whether the market benefits from too many dollar-backed instruments that ultimately serve the same economic function. “We don’t need, you know, 50 US dollar stablecoins. Like, why? Like, they’re all, it’s still, at the end of the day, a U.S. dollar,” he said.

That does not mean he sees no room for differentiation. Instead, he argued that trust, licensing and reserve transparency will become the real competitive variables as the market matures. Ripple, he said, has deliberately taken a compliance-first route, pursuing not just a New York Department of Financial Services license but also an OCC license.

He added that the sector as a whole needs more regulatory verification and disclosure, pointing even to Tether’s renewed push for an audit as evidence that transparency is becoming harder to avoid.

Garlinghouse was similarly upbeat on the US policy backdrop. He described passage of the Genius Act as a major unlock for demand and said corporate executives are now actively asking whether stablecoins should be part of their operations. While he said follow-on legislation around asset classification has been slower, he argued the tone in Washington has already shifted sharply, citing recent coordination between the SEC and CFTC and predicting further progress by the end of May.

“So I think we already have made huge progress in this administration to provide some of that structure and Clarity [Act]. I think clarity will still pass. I was in Washington two days ago, and I think we’ll still get something. [...] I’ll predict by the end of May we’ll get something across,” Garlinghouse said.

At press time, XRP traded at $1.36.

XRP drops below the 200-week EMA again, 1-week chart | Source: XRPUSDT on TradingView.com

Perguntas relacionadas

QWhat was the main reason Ripple decided to launch its own stablecoin, RLUSD?

ARipple decided to launch RLUSD because its role in stablecoin flows had grown so large that building its own product became the logical next step. The company was already minting 20% of all USDC and processing over $100 billion in payment flows, making it a major engine behind stablecoin usage.

QAccording to Brad Garlinghouse, what event in the stablecoin market highlighted the importance of a strong balance sheet for institutional users?

AGarlinghouse pointed to USDC's temporary depeg during the Silicon Valley Bank collapse as the event that highlighted the importance of balance-sheet strength for institutional users, noting that Circle lacked the balance sheet to guarantee the peg at that time.

QHow does Garlinghouse compare the current state of the crypto industry to the internet in the 1990s?

AGarlinghouse compared the current state of crypto to the internet industry in the late 1990s, where companies led with the technology rather than the use case. He stated that just as we no longer talk about 'internet companies' because the technology is now prevalent in the background, blockchain and crypto-based solutions are heading in the same direction.

QWhat does Garlinghouse believe will be the key competitive differentiators in the mature stablecoin market?

AGarlinghouse believes that trust, licensing, and reserve transparency will become the real competitive variables as the stablecoin market matures.

QWhat positive developments in US policy and regulation did Garlinghouse highlight?

AGarlinghouse was upbeat on US policy, citing the passage of the Genius Act as a major unlock for demand. He also mentioned improved coordination between the SEC and CFTC and predicted that further legislative clarity would be achieved by the end of May.

Leituras 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.

marsbitHá 36m

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

marsbitHá 36m

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.

marsbitHá 41m

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

marsbitHá 41m

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.

marsbitHá 41m

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

marsbitHá 41m

Trading

Spot
活动图片