Are Algorithmic Stablecoins Dead? Frax Founder's Latest View on the Stablecoins Industry

CointelegraphОпубліковано о 2022-07-24Востаннє оновлено о 2022-07-25

Анотація

Kazemian says that pure algorithmic stablecoins ”just don’t work.”

Stablecoin projects need to take a more collaborative approach to grow each other’s liquidity and the ecosystem as a whole, says Sam Kazemian, the founder of Frax Finance.
Speaking to Cointelegraph, Kazemian explained that as long as stablecoin “liquidity is growing proportionally with each other” through shared liquidity pools and collateral schemes, there won’t ever be true competition between stablecoins.
Kazemian’s FRAX stablecoin is a fractional-algorithmic stablecoin with parts of its supply backed by collateral and other parts backed algorithmically.
Kazemian explained that growth in the stablecoin ecosystem is not a "zero-sum game" as each token is increasingly intertwined and reliant on each other's performance. 
FRAX uses Circle’s USD Coin (USDC) as a portion of its collateral. DAI, a decentralized stablecoin maintained by the Maker Protocol, also uses USDC as collateral for more than half of the tokens in circulation. As FRAX and DAI continue to expand their market caps, they will likely need more USDC collateral.
However, Kazemian pointed out that if one project decides to dump another, it could have negative effects on the ecosystem.


“It’s not a popular thing to say, but if Maker dumped its USDC, it would be bad for Circle because of the yield they’re earning from them.”


USDC is key
The current top three stablecoins by marketcap in order from the top are Tether (USDT), USDC, and Binance USD (BUSD). DAI and FRAX are both decentralized stablecoins that take the fourth and fifth places among the top.
USDC has had the largest growth over the past year of all three, with market cap more than doubling last July to $55 billion, bringing it nearly within arm’s reach of USDT according to CoinGecko.
Kazemian feels that USDC’s proliferation across the industry and arguably greater transparency about its reserves should make it the most valuable stablecoin for collaboration within the ecosystem.
He called USDC a “low-risk and low-innovation project,” and acknowledged that it serves as the base layer for further innovation from other stablecoins. He said:


“We and DAI are the innovation layer on top of USDC, like the decentralized bank on top of a classical bank.”


Algo stablecoins don’t work
Though the FRAX stablecoin is partially stabilized algorithmically, Kazemian says that pure algorithmic stablecoins ”just don’t work.”
Algorithmic stablecoins like Terra USD (UST), which collapsed in a dramatic fashion in May, maintain their peg through complicated algorithms that adjust supply based on market conditions rather than traditional collateral.


“In order to have a decentralized on-chain stablecoin it needs to have collateral. Doesn’t need to be overcollateralized like Maker, but it needs exogenous collateral.”


The death spiral in Terra’s ecosystem became evident when UST, which is now known as USTC, lost its peg.
The protocol started minting new LUNA tokens to ensure there were enough tokens backing the stablecoin. Rapid minting drove down the price of LUNA, now known as LUNC, which sparked a complete retail sell-off of tokens, dooming any hopes of re-peg.
Related: Liquidity protocol uses stablecoins to ensure zero impermanent loss
In the weeks leading up to the UST depeg, Terraform Labs founder Do Kwon stated that his project needed to fractionally back the stablecoin with different forms of collateral, especially BTC.


“At the end, even Terra realized that their model wouldn’t work,” Kazemian added, “so they started buying up other tokens.”


By the end of May, Terra had sold nearly all of its $3.5 billion worth of BTC.
Terra took down other projects in its wake, including fellow algo stablecoin DEI from Deus Finance, which also has failed to return to the dollar peg as of the time of writing.

Пов'язані матеріали

My Coding Betting Dashboard is Profiting, but Polymarket is Truly Not a Good Place for 'Arbitrage'

The author built a custom monitoring dashboard for Polymarket, a prediction market platform, and tested it with $1,600, achieving over 30% returns. However, the core argument is that Polymarket is not a good venue for traditional arbitrage. The dashboard has two main sections: a "Portfolio Dashboard" for tracking active positions with key metrics like total capital, P&L, and a risk-control module using a tier system (T1, T2, T3), and an "Opportunity Watchlist" for monitoring markets. The article details a critical structural trap in binary markets: a bet with a high perceived probability of success still carries a 100% loss risk if wrong. The author's T1/T2/T3 system is designed to manage this by limiting position sizes based on conviction and time horizon, emphasizing that high confidence should not equal high concentration. A key insight is the danger of "pseudo-diversification"—betting on different markets driven by the same underlying variable. The author concludes that Polymarket offers few true low-risk, arbitrage opportunities. It is instead a high-risk environment where wins can create a false sense of mastery, leading to large losses. The platform is better viewed as a training ground for honing judgment through disciplined, framework-driven betting rather than a reliable income source. The tools help transform intuition into structured, rule-based decisions to mitigate the risk of catastrophic errors.

marsbit1 год тому

My Coding Betting Dashboard is Profiting, but Polymarket is Truly Not a Good Place for 'Arbitrage'

marsbit1 год тому

WeChat AI Card Hands-On Guide: Has the AI Shopping Era Arrived?

**"WeChat AI Card" Practical Test Guide: Has the Era of AI Shopping Arrived?** WeChat has officially launched the "AI Exclusive Card," a feature integrated into its Workbuddy AI assistant. This card is designed to handle payments for AI-initiated purchases. Our hands-on test reveals it's not yet a tool for fully autonomous AI shopping, but rather a controlled payment layer for AI agents. The AI Card functions as an isolated sub-wallet within WeChat Pay. Users must bind the card and transfer funds into it from their main wallet. Crucially, every transaction requires explicit user confirmation via smartphone scan; AI cannot spend autonomously. Currently accessible through the Workbuddy agent, the card targets specific digital consumption scenarios: purchasing paid content (reports, data), calling paid APIs/tools, and subscribing to services. Its design prioritizes security and control by separating funds and mandating approval for each payment. We tested a real-world scenario: ordering bubble tea via Workbuddy using a "Meituan Life Assistant" skill. The process encountered multiple hurdles: high "skill" usage costs (exceeding daily free credits), and most importantly, while a payment was successfully initiated, the AI purchased an incorrect product (a mismatched group-buy coupon instead of the desired drink). This highlights the current limitation: the **AI Card only solves the payment step**. The broader challenge lies in the **AI agent's execution chain**—accurately understanding intent, navigating third-party platforms, selecting the right product, and ensuring proper fulfillment. The payment succeeded, but the purchase failed to meet the user's need. In conclusion, the WeChat AI Exclusive Card is a cautious, early-step experiment in AI commerce. It provides a secure, user-controlled payment method for agent interactions but is not yet capable of reliable, end-to-end complex purchases. For now, it's best used for low-value, low-risk digital services with careful user verification at each step. The vision of AI handling complete shopping tasks remains a work in progress.

marsbit4 год тому

WeChat AI Card Hands-On Guide: Has the AI Shopping Era Arrived?

marsbit4 год тому

Deconstructing Notion's Growth: From a Note-taking Tool to 100 Million Users—How Notion Built a Triple Growth Flywheel Through Product, Templates, and Community

Notion's growth from a niche note-taking tool to a platform with 100 million users is powered by three interconnected flywheels: Product-Led Growth (PLG), a Template Economy, and Community-Driven Growth. First, Notion's PLG strategy relies on a highly flexible, "plastic" product that users can adapt to countless personal and team workflows. Its freemium model lowers the barrier to entry, while features like page sharing and collaboration drive organic, usage-based viral growth as users naturally invite others. Second, the Template Economy solves the "blank page" problem. Templates, created by both Notion and its community, transform abstract product capabilities into concrete, copyable solutions for specific scenarios (e.g., project management, content calendars). This dramatically lowers activation costs for new users and fuels SEO-driven discovery. Third, a vibrant Community acts as a distributed growth engine. Users and official Ambassadors create tutorials, share use cases, and host local events. This community not only educates users but also fosters a sense of identity around pursuing "better ways of working," strengthening loyalty and enabling global, low-cost expansion. Together, these flywheels create a self-reinforcing ecosystem: a great product attracts users who create templates and community content, which in turn attracts more users and deepens engagement. This system allowed Notion to scale from individuals to teams and enterprises through a bottom-up adoption path. Looking ahead, AI integration promises to accelerate these flywheels further by making templates smarter and the platform a potential AI-native work operating system. Ultimately, Notion's defensible advantage is not just its features, but this deeply entrenched network of user assets, creators, and community trust.

marsbit4 год тому

Deconstructing Notion's Growth: From a Note-taking Tool to 100 Million Users—How Notion Built a Triple Growth Flywheel Through Product, Templates, and Community

marsbit4 год тому

$10 Billion, Qualcomm to Acquire Chip Legend Jim Keller's Company

Global mobile chip giant Qualcomm is in advanced talks to acquire AI chip startup Tenstorrent in a deal valued between $8-10 billion, according to media reports. This potential acquisition would be one of the largest in the AI chip sector in recent years. Tenstorrent, led by legendary chip architect Jim Keller, has gained prominence for its RISC-V architecture and AI accelerator designs. The move highlights Qualcomm's strategic push to diversify beyond its core smartphone chip business. As the smartphone market matures, Qualcomm is aggressively targeting growth in automotive, data center, and cloud AI. Acquiring Tenstorrent would allow Qualcomm to rapidly enter the high-end AI computing market, bypassing lengthy in-house development cycles. Tenstorrent's cost-effective system architecture, which avoids expensive HBM memory and relies on standard Ethernet for clustering, offers a potential alternative to Nvidia's costly solutions. Furthermore, Tenstorrent's high-performance RISC-V CPU technology and its focus on the automotive and edge computing segments align with Qualcomm's strategic goals, including its "Snapdragon Digital Chassis" platform. Despite the strategic rationale, the high valuation has sparked some investor caution. The successful integration of Tenstorrent's open-source culture and independent team into Qualcomm's organization, along with the commercialization of its technology, remains a key challenge.

marsbit5 год тому

$10 Billion, Qualcomm to Acquire Chip Legend Jim Keller's Company

marsbit5 год тому

Торгівля

Спот
Ф'ючерси
活动图片