Vitalik Buterin Says ETH-Backed Algorithmic Stablecoins Qualify as ‘True DeFi’

TheNewsCryptoPublicado a 2026-02-09Actualizado a 2026-02-09

Resumen

Vitalik Buterin, co-founder of Ethereum, argues that well-designed ETH-backed algorithmic stablecoins represent "true DeFi" because they structurally transfer U.S. dollar counterparty risk from users to market makers. He criticizes USDC-based yield strategies for relying on centralized infrastructure and failing to embody core DeFi principles. Buterin emphasizes that algorithmic models using smart contracts for collateralized debt positions offer a structural advantage over fiat-backed stablecoins by minimizing central counterparty risk. While acknowledging challenges like oracle and peg stability risks, he asserts that these mechanisms do not undermine their decentralized foundation. This perspective shifts focus from yield generation to risk architecture in stablecoin design and DeFi innovation.

Ethereum co-founder Vitalik Buterin argued that even well-designed ETH-collateralized algorithmic stablecoins still constitute genuine decentralized finance. He clarified that such algorithmic mechanisms can transfer U.S. dollar counterparty risk from users to market makers.

Buterin blasted the notion that USDC deposit yield strategies are representative of true DeFi principles. He suggested that the meaningful shifting of counterparty risk significantly enhances stability when compared with simple fiat-backed models. The point of contention is essentially about the means of risk structure and not yield generation within DeFi protocols. Buterin, in a post on X, a social platform, had stated that critics misunderstand DeFi’s origins and purposes in essentially focusing on yield alone.

Buterin, in turn, noted that stablecoins based on algorithms employ smart contract-based collateralized debt positions. Buterin claimed that such positions can establish a structural advantage over fiat-based stablecoins. He argued that, through using these stablecoins, it is possible to avoid counterparty risks, sending them to market makers instead. In essence, there was a significant structural value to doing this. Most opponents of stablecoins point to sources such as CDP holders and arbitrage positions. Nevertheless, Buterin claimed that these do not erase their DeFi basis.

Defi’s Principles and Stablecoin Risk

While explaining the difference between algorithmic and central USD-pegged strategies that rely upon external providers, such as Circle, Buterin emphasized that it is important for the stablecoin protocol to seek ways to minimize central counterparty risk. Buterin also mentioned that it is likely that future protocol models might include diversified real-world assets. In other words, assets other than one benchmark may reduce risks. Buterin mentioned that current USDC-based yield strategies do not change assumptions about trust.

These strategies still depend on centralized infrastructure. Buterin’s comments have come as the crypto market is still experimenting with innovations around stablecoins. The disagreement reflects the schism over the way DeFi should take shape. Major challenges with algorithmic stablecoins are oracle risks and peg stability over time. It is such structural risks that require a strong design to ensure resilience in the long term. Buterin’s framing would further drive attention to the risk architecture inside the stablecoin rather than its yield mechanics. This discussion will likely influence future stablecoin design and decentralized finance innovation.

Highlighted Crypto News:

Lyn Alden Says Fed Entering ‘Gradual Print’ Era of Monetary Policy

TagsCryptocurrencyDeFiETHETHEREUMEthereum (ETH)StablecoinVitalikvitalik ButerinVitalikButerin

Preguntas relacionadas

QAccording to Vitalik Buterin, what qualifies as 'True DeFi' in the context of stablecoins?

AETH-backed algorithmic stablecoins that use smart contract-based collateralized debt positions to transfer counterparty risk from users to market makers.

QWhat key risk does Buterin argue is shifted when using algorithmic stablecoins compared to fiat-backed models?

ACounterparty risk, specifically U.S. dollar counterparty risk, is transferred from users to market makers.

QWhat does Buterin criticize as not being representative of true DeFi principles?

AUSDC deposit yield strategies, because they still depend on centralized infrastructure and don't change assumptions about trust.

QWhat structural advantage do algorithmic stablecoins have over fiat-based stablecoins according to Buterin?

AThey can establish a structural advantage by avoiding counterparty risks through their design that uses smart contract-based collateralized debt positions.

QWhat future development did Buterin mention regarding protocol models and risk reduction?

AFuture protocol models might include diversified real-world assets beyond a single benchmark to reduce risks.

Lecturas Relacionadas

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

Silicon Valley investor and "Godfather of Startups" Steve Hoffman warns that combining Web3 with AI is likely a trap, not a promising venture. In an interview, Hoffman argues that while AI is a foundational technology touching all industries, Web3 adds complexity, friction, and regulatory risk without solving mainstream consumer or business needs. He advises founders to focus on deep, specialized applications where startups can out-iterate giants, rather than on generic features easily replicated by large tech companies. Hoffman observes that Silicon Valley will lead foundational AI research, while China excels at rapid, large-scale application and commercialization, particularly in robotics. He stresses that AI-driven autonomous agents capable of collaborative, multi-step tasks are 2-4 years away, which will cause significant job displacement. The solution is not to slow AI but to redesign business models around human-AI collaboration and reform social systems like education and retraining. For startups, Hoffman recommends focusing on vertical, expertise-heavy domains to build defensibility. He sees major opportunities in AI fraud detection and cybersecurity. Key founder mindsets include systemic thinking over feature-focus, relentless customer centricity, building adaptive teams, and deeply understanding AI's capabilities and limits. Hoffman is also leading a non-profit initiative to establish university centers aimed at training future leaders in responsible, human-value-aligned AI innovation.

marsbitHace 3 min(s)

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

marsbitHace 3 min(s)

Token Inefficient, Economy Tokenless

The article "Tokens Aren't Economical, Economics Aren't Tokenized" analyzes a pivotal shift in the AI industry from a technology-driven narrative to one dominated by capital efficiency. It highlights two concurrent trends: a severe capital shortage due to the exorbitant and recurring costs of compute (e.g., OpenAI's high burn rate) and a wave of corporate spin-offs where major tech companies are separating their AI units (like Kuaishou's Kling and Baidu's Kunlunxin). The core argument is that AI's "anti-internet" business model, where user growth increases costs rather than profits, has created a disconnect between high valuations and actual cash flow. Spin-offs address this by allowing AI assets to be valued independently. Within a parent company, they are seen as cost centers, but as standalone entities, they are priced based on their growth potential and scarcity in the primary market, leading to massive valuation premiums (e.g., Kling's estimated value tripling post-spin-off). The industry is at an inflection point, moving from "model worship" to "value realization." The competition is evolving from a pure compute (GPU) race to a broader focus on systemic efficiency and full-stack engineering (involving CPUs and orchestration) to achieve viable commercialization. The year 2026 is framed as a critical moment where the industry must definitively answer how to economically translate AI capability into tangible business value, reshaping the sector's future power structure.

marsbitHace 8 min(s)

Token Inefficient, Economy Tokenless

marsbitHace 8 min(s)

Crossing the 'Memory Wall': The Wafer-Level Revolution and Computing Power Routes in the AI Inference Era

In 2026, a historic shift occurred in AI as major cloud providers' inference spending surpassed training spending for the first time, signaling a move from "building large models" to "using large models." This shifts the core challenge from computing power to the "memory wall"—the bottleneck of data movement (model weights, activations, KV Cache) between external DRAM and processors, where energy and latency from data transfer far exceed computation itself. Companies like Nvidia face GPU idle time due to bandwidth limits. In contrast, Cerebras Systems adopts a radical "wafer-scale" approach with its Wafer-Scale Engine (WSE). Instead of cutting a silicon wafer into many chips, Cerebras uses almost the entire wafer as one massive chip (WSE-3). This design provides 44GB of on-chip SRAM, delivering memory bandwidth thousands of times higher than traditional HBM (e.g., 21 PB/s vs. Nvidia B200). For LLM inference, weights are streamed layer-by-layer from external MemoryX storage to the chip, avoiding HBM bottlenecks. This results in token generation speeds 1.5–5 times faster than Nvidia's B200 in some models and significant advantages in first-token latency and long-context tasks. Additionally, Cerebras's architecture offers much lower interconnect power consumption (0.15 pJ/bit vs. GPU's ~10 pJ/bit). However, Cerebras faces challenges: SRAM scaling has slowed with advanced nodes, limiting future capacity gains; the chip requires specialized liquid cooling and custom software stacks; and its external I/O bandwidth (150 GB/s) is low compared to NVLink, hindering multi-system scaling for very large models. Competition is intensifying. Major players are pursuing three paths: 1) Developing proprietary inference ASICs (e.g., Google TPU, Microsoft Maia), 2) Leveraging advanced packaging (e.g., TSMC's SoW) to democratize wafer-scale-like integration, potentially eroding Cerebras's process advantage within a few years, and 3) Exploring optical interconnects for ultimate bandwidth. Commercially, Cerebras is transitioning from a hardware vendor to a service provider, facing the immense challenge of building high-power, specialized data centers to meet large contracts (e.g., 250MW/year from 2026–2028). In conclusion, the AI inference era presents a fundamental architectural trade-off. Cerebras opts for extreme physical optimization for low-latency, single-task performance, while Nvidia prioritizes versatility and massive cluster throughput. The path forward remains uncertain, with technology and business models still evolving in the race toward advanced AI.

marsbitHace 14 min(s)

Crossing the 'Memory Wall': The Wafer-Level Revolution and Computing Power Routes in the AI Inference Era

marsbitHace 14 min(s)

Has Bitcoin's 'Rebound Ended', Officially Entering the Late Bear Market Phase?

**Title: Has Bitcoin's Rebound Ended, Entering the Late Bear Market Phase?** **Summary:** Bitcoin's price has declined by 13% this week, signaling a potential return to late-stage bear market conditions. The price fell to around $67k, positioned between the Realized Price and Realized Cap Weighted Average. For the first time since early 2022, the Short-Term Holder cost basis has dropped below this key average, confirming a hallmark of late-cycle bear markets. Profitability metrics have collapsed sharply. The 7-day average of the Realized Profit/Loss ratio plummeted from a local high of 3.16 to 0.29, mirroring the February panic sell-off. Critically, the 90-day average never breached the threshold of 2, indicating the recent rally to $82k was a bear market bounce, not a structural shift. Realized losses surged to $1.35 billion daily, with $770 million coming from Long-Term Holders selling at a loss. This accelerating redistribution of supply from weak to strong hands is a necessary but ongoing process for a market bottom. The rally stalled almost precisely at the aggregate cost basis (~$83k) of US spot Bitcoin ETF investors, turning that level into strong resistance and leaving the average ETF holder underwater again. Spot market flows have turned decisively negative, showing sellers are dominating order books despite the price drop. While a significant futures long liquidation event cleared over $400 million in leverage, providing a potential reset, sustained spot demand is yet to materialize. Options markets continue to price in higher future volatility (Implied Volatility) than recent price action (Realized Volatility) has shown, with a persistent skew towards put options, indicating ongoing demand for downside protection. In conclusion, multiple metrics point to a fragile market structure. Resistance at the ETF cost basis, accelerating realized losses, dominant spot selling, and cautious options pricing all suggest the bear market trend persists. A sustainable recovery likely requires a resurgence of spot demand, ETF holders returning to profit, and a clear reduction in selling pressure.

marsbitHace 14 min(s)

Has Bitcoin's 'Rebound Ended', Officially Entering the Late Bear Market Phase?

marsbitHace 14 min(s)

TechFlow Intelligence Agency: Anthropic Calls for Global Pause in AI Development While Preparing for Trillion-Dollar IPO; SpaceX IPO Roadshow Heats Up, But S&P 500 Rejects Fast-Track Inclusion

In today's TechFlow Intelligence Briefing, several major tech stories highlight a growing theme of trust and credibility gaps across AI, crypto, and finance. AI company Anthropic has publicly called for a global pause in AI development, citing risks from Claude's "recursive self-improvement." Ironically, this coincides with reports the company is preparing for a massive IPO targeting a near $1 trillion valuation. This perceived hypocrisy, coupled with widespread user complaints about Claude's declining performance, is sparking debate over whether the safety warning is genuine or a competitive tactic. Meanwhile, in a substantive security move, Anthropic open-sourced a framework for AI-powered vulnerability discovery. In the crypto market, Bitcoin's price drop below $61,000 triggered over $1.16 billion in liquidations, flipping the market into a state where more BTC is held at a loss than at a profit, a historical bearish signal. On the corporate front, SpaceX's highly anticipated IPO is generating immense Wall Street excitement, with Goldman Sachs projecting 100x revenue growth by 2030. However, the S&P 500 has refused to fast-track the company's inclusion post-IPO, potentially limiting immediate institutional demand. Separately, ByteDance's AI app Doubao lost over 6 million monthly active users after introducing a subscription model, highlighting the challenges of AI monetization. Other notable developments include Nvidia certifying HBM4 memory from Samsung, SK Hynix, and Micron; Cloudflare's acquisition of front-end tooling company VoidZero; and its CEO warning that bot traffic now exceeds human traffic online. The underlying narrative connects these events: a trust crisis. From AI firms' contradictory actions and crypto volatility to the clash between SpaceX's hyped narrative and institutional rules, a pattern is emerging where stated intentions and actual practices are increasingly misaligned.

marsbitHace 29 min(s)

TechFlow Intelligence Agency: Anthropic Calls for Global Pause in AI Development While Preparing for Trillion-Dollar IPO; SpaceX IPO Roadshow Heats Up, But S&P 500 Rejects Fast-Track Inclusion

marsbitHace 29 min(s)

Trading

Spot
Futuros
活动图片