BTC后市的三种推演与两强对决 | 特邀分析

Vitalik's Latest Long Read: In the AI Era, How Can Code Become More Secure?

Vitalik Buterin explores the role of formal verification as a critical tool for software security, especially in the AI era and for blockchain systems. He defines formal verification as using machine-checkable mathematical proofs to verify that code meets specified properties, moving beyond manual auditing. The article highlights that while AI can generate code and find vulnerabilities rapidly, it also makes formal verification more accessible by assisting in writing proofs. This is crucial for Ethereum's complex components like STARKs, ZK-EVMs, consensus algorithms, and high-performance EVM implementations, where bugs can lead to irreversible losses. Vitalik argues that formal verification enables a powerful "separation of concerns": AI can write highly optimized (e.g., assembly) code for efficiency, while a separate, human-readable specification defines correctness. A machine-checked proof then verifies their equivalence. This paradigm can create a more secure "trusted core" of software. However, he cautions that formal verification is not a panacea. "Proven correctness" depends on the accuracy of the specifications and proofs themselves, which can be wrong or incomplete. Risks include unverified code sections, hardware-level side-channel attacks, and overlooked assumptions. The true goal is not absolute proof but increased confidence through redundant expressions of intent—using code, tests, types, and formal proofs—and automatically checking their consistency. The article concludes that AI and formal verification are complementary: AI enables scale, while verification ensures accuracy. For critical systems, this combination offers a path toward stronger security in a future with powerful AI adversaries, helping to maintain the defensive advantage essential for a decentralized internet.

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海面收比特币，海下扬言断网，伊朗正“绞杀”美元体系

Odaily星球日报Há 11m

IOSG: After the Number of Developers Halved, Crypto Did Not Die

The crypto development community has undergone a significant transformation, with monthly active developers on GitHub halving from 45K in 2022 to approximately 23K by 2026. This decline is largely attributed to the departure of newcomers, whose roles were often tied to market-driven hype cycles like NFTs and forked DeFi protocols, leading to a 52% churn rate among those with less than a year of experience. However, the core of the industry has strengthened. Established developers with over two years of experience have reached a record high, contributing about 70% of the code. They are consolidating around ecosystems with genuine users and revenue, such as Bitcoin and Solana, while moving away from narrative-driven projects. The talent shift represents a "deleveraging" and an increase in core density. This core group has developed a unique skillset by operating in an environment of "code is law," with zero tolerance for error and no external recourse. They have learned to build trust and functional systems from the ground up without central authorities, as demonstrated by protocols like Uniswap and MakerDAO. These capabilities are now being repriced and leveraged in the AI era. The structural challenges of AI scaling—such as trust, coordination, and verification—mirror those long addressed in crypto. Examples include CoreWeave pivoting from GPU mining to AI compute, OpenSea's founder applying NFT market logic to AI model routing with OpenRouter, and projects like NEAR and Catena Labs transitioning crypto-native architectural and financial insights into AI infrastructure and agent banking. Key areas where crypto-bred skills are directly applicable to AI include: 1. **Compute Aggregation & Optimization**: Using token incentives and cryptographic verification (e.g., Proof of Sampling & Privacy) to create trusted, decentralized GPU networks, as seen with Hyperbolic. 2. **AI Governance & Incentive Design**: Applying economic mechanism design from DAOs and tokenomics to align the goals of multiple, fast-acting AI agents, a direction explored by EigenLayer's EigenCloud. 3. **AI Agent Autonomous Payments**: Leveraging stablecoins and programmable, permissionless blockchains to enable the micro-transactions required for AI agent economies, exemplified by protocols like x402. The role of the crypto builder is evolving from writing smart contracts to designing trust mechanisms for autonomous AI systems. This convergence is reflected in hiring trends at major firms and significant capital allocation from funds like Paradigm and a16z crypto, which are investing at the intersection of crypto and AI. Regional differences exist, with the US favoring foundational protocol innovation and Asia focusing on compliant application-layer integration, but the underlying trend is clear. The industry's "deleveraging" has not signaled its demise but rather a maturation, positioning its core builders to solve critical trust and coordination problems in the age of AI.

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Currency and Stock Market Barometer: Strategy Invested Over $2 Billion to Buy Over 24,800 BTC Last Week; Bitmine's ETH Holdings Increase to 4.37% of Total Supply (May 19)

Crypto & Stock Market Watch: Institutional BTC Buying Surges, ETH Holdings Grow Major listed companies aggressively accumulated Bitcoin last week, with net purchases skyrocketing over 44x to $2.03 billion. Strategy (formerly MicroStrategy) led the charge, spending approximately $2.01 billion to buy 24,869 BTC, bringing its total holdings to 843,738 BTC. Overall, listed firms (excluding miners) now hold 1,113,841 BTC, valued at ~$86.16 billion. On the Ethereum front, Bitmine purchased 71,672 ETH in the past week. It now holds 5,278,462 ETH, worth $11.56 billion and representing 4.37% of ETH's total supply. A significant portion (4,712,917 ETH) is staked, generating an annualized yield of $289 million. Industry leaders note a divergence from the MicroStrategy model, with ETH treasury firms increasingly focusing on staking yields and simpler balance sheets. In traditional markets, Morgan Stanley warns of a potential significant U.S. stock market correction if bond yields and volatility continue rising. Investment giants like Berkshire Hathaway and Bridgewater adjusted portfolios in Q1, with Bridgewater notably increasing its stakes in chipmakers like Nvidia, Broadcom, and Micron while shedding software stocks. Among other crypto-focused public companies, Solana treasury firm Upexi reported a widened net loss of $109 million for its fiscal Q3, driven by a decline in its crypto holdings' value. Meanwhile, Hyperion DeFi, a HYPE token treasury company, reported a Q1 net profit of $8.8 million and increased its HYPE holdings past 2 million tokens.

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24-Year-Old "Wall Street Newcomer" Portfolio Adjustments Revealed: Shorts Chips Heavily in Q1, Bullish on Energy and AI Infrastructure

A 24-year-old Wall Street prodigy, Leopold Aschenbrenner, has disclosed the Q1 portfolio adjustments for his fund, Situational Awareness LP. The fund's assets under management skyrocketed from $5.52 billion to $13.7 billion. The most significant move was a massive bearish bet on the semiconductor sector. The fund established $8.46 billion in put options, targeting chipmakers like NVIDIA ($1.6B in puts) and the VanEck Semiconductor ETF (SMH, $2B in puts). This bearish stance extended to Broadcom, Oracle, AMD, Micron, ASML, Intel, Corning, and TSMC. However, the fund made a selective bullish exception, adding shares and call options for memory chip maker SanDisk. The fund maintained its core bullish thesis on energy and AI infrastructure. Bloom Energy remained its largest long equity holding. It also increased stakes in cryptocurrency mining/data center firms like CleanSpark, Riot Platforms, Applied Digital, and IREN Limited, viewing them as providers of critical ready-to-use infrastructure—land, power, and grid permits—for AI expansion. The 13F filing was submitted one day late. Overall, the fund's strategy involves substantial bearish semiconductor bets while maintaining concentrated, high-volatility investments in selective tech, computing, and infrastructure plays aligned with AI growth.

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