Indepth Research

Provide in-depth research reports and independent analysis, leveraging data, technology, and economic insights to deliver a comprehensive examination of the blockchain ecosystem, project potential, and market trends.

After $1.26 Trillion: Why Are Circle and Stripe Rushing to Pay 'Wages' to AI Agents?

The article discusses the significant rise of stablecoins, particularly USDC, as the preferred payment method for AI agents. In March 2026, Circle and Stripe are competing to build stablecoin infrastructure for AI agent payments, with USDC processing $1.26 trillion in transactions, accounting for 70% of stablecoin activity. Key points include: - AI agents require programmable, instant, low-friction payment systems, which traditional finance (banks, credit cards) cannot provide. Stablecoins on blockchain meet these needs with 24/7 transfers, smart contract automation, and price stability. - Data shows 98.6% of AI agent payments on platforms like Stripe's x402 use USDC, indicating stablecoins are becoming the default for machine-to-machine transactions. - Regulatory developments are supporting this growth: Hong Kong is issuing its first stablecoin licenses, the US OCC has proposed a federal framework, and the EU has MiCA regulations, signaling global institutional adoption. - Stablecoins act as a "blood system" connecting the digital and real economies, facilitating both internal digital transactions (e.g., tokenized assets) and external fiat conversions. - Risks include security vulnerabilities, regulatory fragmentation, and market instability, but the trend is clear: stablecoins are evolving from crypto tools to essential infrastructure for AI-driven economies. The article concludes that as AI agents autonomously transact, stablecoins will be critical infrastructure, urging businesses and investors to prepare for this shift.

marsbit2 ч. назад

After $1.26 Trillion: Why Are Circle and Stripe Rushing to Pay 'Wages' to AI Agents?

marsbit2 ч. назад

50 Million USDT for 35,000 USD worth of AAVE: How Did the Disaster Happen? And Who Should We Blame?

In a catastrophic DeFi transaction, a user swapped 50.43 million aEthUSDT (Aave interest-bearing USDT) for only 327.24 aEthAAVE (worth ~$35,900), resulting in a near-total loss of value. The transaction was a collateral swap executed via CoW Protocol’s settlement system and Aave’s interface. The failure occurred due to a deeply flawed routing path: after redeeming USDT from Aave, the funds were routed through a highly liquid Uniswap V3 USDT/WETH pool (correctly executing the first swap). However, the entire amount of ~17,958 WETH was then sent to a tiny SushiSwap V2 AAVE/WETH pool with only ~331 AAVE and ~17.65 WETH in reserves. The massive trade drained 99.9% of the pool's AAVE, resulting in an effective execution price of ~$154,114 per AAVE—over 1000x worse than market price. Critical systemic failures were identified: 1. Aave’s interface requested a CoW quote without including critical hook metadata, leading to an inaccurate quote. 2. CoW’s solver competition logic deemed any quote with non-zero output and positive gas cost as "valid," with no sanity checks against market price or liquidity depth. 3. The routing algorithm modeled the tiny SushiSwap pool as a valid execution venue purely based on its constant-product formula, ignoring the economic absurdity. 4. Aave’s UI only provided a soft warning (a checkbox) for high price impact instead of a hard stop. The lost value was instantly arbitraged in the next block, benefiting MEV searchers and block builders. The core protocols (Aave, CoW Settlement, Uniswap, SushiSwap) functioned as coded. The primary blame lies with CoW’s inadequate routing quality controls and Aave’s flawed interface quote generation and weak risk safeguards.

Odaily星球日报13 ч. назад

50 Million USDT for 35,000 USD worth of AAVE: How Did the Disaster Happen? And Who Should We Blame?

Odaily星球日报13 ч. назад

Matrixport Research: After Five Consecutive Months of Bitcoin Decline, Conditions for a Market Rebound Are Gradually Forming

Matrixport Research: Conditions for a Market Rebound Gradually Forming After Bitcoin's Consecutive Five-Month Decline Amid low trading volumes and weak market sentiment, with many investors shifting focus to traditional assets like gold and oil, underlying market conditions are quietly improving. Bitcoin has declined for five consecutive months—a historically rare occurrence—which has often preceded阶段性反弹 (stage-wise rebounds) in the past. Similarly, the total market cap of altcoins has fallen to a range that has historically triggered multiple rebound initiations. Although the overall altcoin model has not yet turned bullish, the number of altcoins reclaiming their 30-day moving average and showing improved momentum through quantitative screening has significantly increased. With stablecoin funds flowing back into the market, overall liquidity conditions are also improving, pointing to a potential market inflection window. From a historical perspective, Bitcoin often experiences阶段性反弹 (stage-wise rebounds) after three consecutive months of decline in a bear market. A sustained decline of four to six months with little recovery is relatively rare. The market is currently in such an extreme sequence, increasing the probability of a short-term counter-trend recovery. Simultaneously, the valuation of the altcoin sector has entered a range where周期性反弹 (cyclical rebounds) have historically been more likely. When the total altcoin market cap deviates approximately 30% from its 90-day moving average, the market is often in a bottom-building phase, followed by sustained recovery in Bitcoin and altcoins. Although trading volume remains low, the price structure of some altcoins has begun to improve, and Bitcoin is potentially building a阶段性底部 (stage-wise bottom) near $66,000. If prices hold the current support zone and gradually break through key resistance levels, the recovery process is expected to continue. Despite the overall weak performance of altcoins this cycle, some structural changes are emerging. More altcoins are reclaiming their 30-day moving average and beginning to outperform Bitcoin—often an early signal of improved market momentum. The number of altcoins selected through quantitative momentum screening has also increased significantly, with some tokens simultaneously exhibiting improved momentum and fundamental catalysts. More importantly, the market funding environment is changing. The previous dynamic dominated by liquidations and capital outflows is gradually shifting towards capital回流 (inflows). The re-expansion of stablecoin liquidity is a key signal; in the past month, Circle's USDC alone recorded approximately $8 billion in net inflows, indicating that capital is re-entering the crypto market. As liquidity gradually improves, the probability of capital being reallocated to Bitcoin and Ethereum is also rising, which will provide support for a broader market. Overall, while crypto market sentiment remains subdued, multiple key conditions are gradually forming. After a historically rare streak of monthly declines, Bitcoin appears to be building a potential bottom; stablecoin funds are回流 (flowing back), improving market liquidity. Simultaneously, the altcoin market breadth is expanding, with more tokens reclaiming their 30-day momentum threshold. Although the altcoin model has not yet officially turned bullish, trading setups meeting screening conditions have risen to their highest level in months. If Bitcoin confirms a trend breakout above key points, the probability of a broader阶段性反弹 (stage-wise rebound) will further increase.

Matrixport16 ч. назад

Matrixport Research: After Five Consecutive Months of Bitcoin Decline, Conditions for a Market Rebound Are Gradually Forming

Matrixport16 ч. назад

From 5 Cents per kWh Chinese Electricity to $45 API Export Packages: Token is Becoming the New Currency Unit

The article explores the concept of "Token出海" (Token Outbound), arguing that tokens are evolving from a technical term into a new monetary unit in the machine-driven economy. It begins by drawing a parallel between historical control over information flow (like transatlantic cables) and today's control over AI API calls and value transfer. Tokens now serve a dual role: as a unit of computation in AI and a means of payment in crypto. A key driver is the rise of AI Agents, like OpenClaw, which shift tokens from being a simple "conversation cost" to a "production fuel" for executing complex tasks. This massive consumption creates a competitive advantage for Chinese AI models, which are often priced lower. The article posits that this isn't just about cheap models, but about China leveraging its vast domestic electricity and computing power to export value globally via token-denominated AI services. The convergence of AI and crypto is facilitated by protocols like x402, which enables machines to natively pay for API calls, and ERC-8183, which allows them to enter into complex escrow-based contracts. This creates a machine-native economic layer where tokens act as the fundamental unit of permission, settlement, and value measurement. The conclusion is that while traditional fiat won't disappear, tokens are becoming the foundational monetary unit for the new agentic economy. The future "power to mint currency" may belong to those who can most efficiently compress real-world resources (like electricity and compute) into tradable tokenized services.

Odaily星球日报22 ч. назад

From 5 Cents per kWh Chinese Electricity to $45 API Export Packages: Token is Becoming the New Currency Unit

Odaily星球日报22 ч. назад

a16z: AI Makes Everyone 10x More Efficient, But No Company Becomes 10x More Valuable

a16z investor George Sivulka argues that while AI has dramatically increased individual productivity by 10x, it hasn’t translated into a 10x increase in company value. The core issue is not the technology itself, but the failure to redesign organizations around it—much like factories in the 1890s initially replaced steam engines with electric motors but didn’t see real gains until they fully redesigned assembly lines decades later. Sivulka distinguishes between “Personal AI” (e.g., ChatGPT) and “Organizational AI,” outlining seven key dimensions where they differ: 1. **Coordination:** Personal AI creates chaos; Organizational AI coordinates teams and agents toward unified goals. 2. **Signal:** Personal AI generates noise and low-quality output; Organizational AI filters noise to find valuable signals. 3. **Bias:** Personal AI reinforces user bias; Organizational AI introduces objectivity and challenges assumptions. 4. **Edge Advantage:** Personal AI optimizes for general usage; Organizational AI leverages domain-specific expertise for competitive advantage. 5. **Outcome:** Personal AI saves time; Organizational AI drives revenue growth. 6. **Enablement:** Personal AI gives a tool; Organizational AI embeds processes and enables organizational change. 7. **Promptless:** Personal AI requires human prompts; Organizational AI acts autonomously without human intervention. True value, Sivulka concludes, will come from rebuilding organizations and processes around AI—not just adopting the technology. The future belongs to companies that build “Organizational AI” systems that integrate deeply with institutional workflows.

marsbit22 ч. назад

a16z: AI Makes Everyone 10x More Efficient, But No Company Becomes 10x More Valuable

marsbit22 ч. назад

How Much Money Has Kalshi Actually Made? Deconstructing the Prediction Market Business Behind 200 Million Trades

In this analysis of Kalshi, a leading prediction market platform, the author examines its business model, transaction data, and regulatory landscape. By accessing Kalshi’s public API, the study reveals that the platform has processed over 203 million transactions with a total volume exceeding $41.7 billion. More than 82% of this volume comes from sports betting, positioning Kalshi as a de facto sports gambling platform accessible to users as young as 18. The platform operates a central limit order book (CLOB) where users trade binary contracts that settle at either $1 (if the event occurs) or $0 (if it does not). Kalshi generates revenue through a variable fee structure: Takers pay a fee based on the formula 0.07 × C × P × (1-P), where C is the number of contracts and P is the price, while Makers pay a quarter of that rate. Total fee income amounts to $545.6 million. Kalshi ecosystem includes markets, events, and series, with major volumes driven by events like the 2024 U.S. presidential election and Super Bowl outcomes. The platform’s fee model is compared to traditional sportsbooks, highlighting how its variable structure adapts to implied probability. Regulatory oversight falls under the CFTC, though enforcement remains limited, creating a grey area that allows Kalshi to operate with fewer restrictions than conventional gambling platforms. The analysis also touches on market结算 practices, liquidity incentives, and the broader context of prediction markets, including competitors like Polymarket and regulatory cases such as PredictIt’s legal battle with the CFTC.

marsbit22 ч. назад

How Much Money Has Kalshi Actually Made? Deconstructing the Prediction Market Business Behind 200 Million Trades

marsbit22 ч. назад

The Fall of Crypto Actually Has Little to Do with Scamming Retail Investors

The decline of Crypto is not primarily due to "scamming retail investors," but stems from deeper structural issues, according to a seasoned Crypto OG. Key problems include: 1. **Misunderstanding of Bitcoin’s Whitepaper**: The core concept is not "decentralization" (a term absent in the whitepaper) but "distributed trust architecture" — eliminating the need for trusted third parties. Many projects fail to achieve even basic distributed systems while overusing decentralized rhetoric. 2. **Loss of Incremental Users**: Grand narratives (Web3, Metaverse, GameFi, etc.) have oversold the technology’s capabilities, leading to repeated user disappointment and eroded trust. The market now suffers from a lack of new participants. 3. **Erosion of Community Belief**: Many communities engage in "narrative engineering" — using complex jargon to attract new users while insiders anticipate selling at peaks. This creates a cycle of hype, pump, and dump, damaging overall market credibility. 4. **Premature Financialization**: Crypto prioritized token launches and financialization before establishing robust infrastructure or mature applications. This led to overvaluation and repeated failures when technology couldn’t support inflated prices. 5. **Shift in Attention**: Human attention is moving from social and community interactions (like Telegram and Discord) toward AI-driven engagement. As an attention-dependent market, Crypto is naturally declining as interest wanes. The OG concludes that while Crypto isn’t dead, its current narrative has ended. The real tragedy is exhausting two decades of storytelling in just three years, before the underlying technology was ready. Scams are inevitable in markets, but the absence of new believers is fatal.

比推Вчера 18:31

The Fall of Crypto Actually Has Little to Do with Scamming Retail Investors

比推Вчера 18:31

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