# Сопутствующие статьи по теме AI

Новостной центр HTX предлагает последние статьи и углубленный анализ по "AI", охватывающие рыночные тренды, новости проектов, развитие технологий и политику регулирования в криптоиндустрии.

Meta's Largest-Scale Layoffs in History: Notifications Sent at 4 AM, 7,000 Employees Reassigned to AI

Meta is executing its largest-ever restructuring, involving the elimination of approximately 8,000 positions (around 10% of its workforce) alongside a strategic shift toward artificial intelligence. Notifications for affected employees are being sent in three waves, timed for 4 AM local time across global offices this Wednesday. The reorganization is not purely a cost-cutting measure. Concurrently, over 7,000 employees are being redeployed to new AI-focused units, including the Applied AI Engineering (AAI) department, as part of a company-wide push to integrate "AI-native design principles." Management layers are being significantly reduced to create a flatter, faster-moving organizational structure. Internal tensions are rising amid the layoffs. Over a thousand employees have signed a petition protesting the company's installation of mouse-tracking software, reflecting strained morale. Meta has indicated that further job cuts beyond this initial 10% are possible. The moves underscore Meta's heavy bet on AI, with capital expenditures projected to reach $125-145 billion by 2026 to fund AI infrastructure.

marsbit05/19 10:17

Meta's Largest-Scale Layoffs in History: Notifications Sent at 4 AM, 7,000 Employees Reassigned to AI

marsbit05/19 10:17

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.

marsbit05/19 09:56

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

marsbit05/19 09:56

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.

marsbit05/19 09:28

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

marsbit05/19 09:28

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.

marsbit05/19 09:09

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

marsbit05/19 09:09

Kraken Slashes 150 Roles While Ramping Up AI Efficiency Measures

Cryptocurrency exchange Kraken has laid off approximately 150 employees as part of efforts to increase operational efficiency through the adoption of artificial intelligence. This move has reportedly pushed back the company's planned U.S. initial public offering (IPO) timeline to at least 2027. Kraken had confidentially filed for an IPO in late 2025 but placed plans on hold earlier this year amid declining crypto market conditions. The layoffs, driven by AI integration, reflect a broader trend in the crypto industry, where firms including Coinbase, Gemini, Crypto.com, and Block have collectively cut thousands of jobs in 2026. A weaker market and reported quarterly losses have added to the sector's pressures. Kraken has not publicly commented on the job cuts or the revised IPO schedule.

bitcoinist05/19 08:03

Kraken Slashes 150 Roles While Ramping Up AI Efficiency Measures

bitcoinist05/19 08:03

Money Has Gone to Bonds and IPOs, Leaving Only HYPE Rising in Crypto

The article "Where Has All the Money Gone? Bonds and IPOs Are Soaring, While Crypto Only Sees HYPE Rising" analyzes the recent underperformance of major cryptocurrencies like Bitcoin and Ethereum compared to traditional financial markets. It identifies three primary factors diverting capital away from crypto: First, surging bond yields, with the 30-year U.S. Treasury hitting a near 20-year high of 5.12%, are attracting capital seeking safe, predictable returns. This is evidenced by Bitcoin spot ETFs experiencing a significant $10.39 billion net outflow in mid-May. Second, a massive $4 trillion IPO pipeline, highlighted by SpaceX's upcoming listing, is absorbing risk capital that might otherwise flow into crypto. Platforms like Hyperliquid are even channeling on-chain crypto liquidity into pre-IPO trading for traditional stocks. Third, uncertainty surrounds new Federal Reserve Chair Warsh's ability to deliver expected interest rate cuts this year due to conflicting political pressures and stubborn inflation expectations, potentially eliminating a hoped-for source of new market liquidity. Consequently, while traditional equities and bonds rally, the crypto market's post-leverage crash recovery is stalled. The notable exception is assets like Hyperliquid (HYPE), which is rising due to its role in facilitating traditional asset trading, underscoring a market divergence where only crypto projects with novel, cross-market narratives are gaining. The article concludes that Bitcoin's next major catalyst may be the August enactment of the CLARITY Act, but warns of a potential retest of the $70,000 support level before then.

marsbit05/19 06:47

Money Has Gone to Bonds and IPOs, Leaving Only HYPE Rising in Crypto

marsbit05/19 06:47

Agents Capital Markets: How Will Autonomous Agents Secure Financing?

Agents Capital Markets: How Will Autonomous Agents Raise Capital? Within a decade, autonomous software agents—legal entities capable of signing contracts, holding bank accounts, and generating revenue—will create their own capital markets. These markets will feature rating agencies, underwriters, indices, and brokers, mirroring traditional public equity markets. Agents will perform routine services like marketing, logistics, and customer support at a fraction of human-operated costs, creating massive economic pressure for adoption. Four converging forces ensure this outcome: 1) Overwhelming cost advantages, with AI inference costs plummeting; 2) Existing, revenue-generating agent companies (e.g., Sierra, Harvey) proving market demand; 3) Established legal frameworks (e.g., Wyoming's memberless LLCs) enabling algorithmic management; and 4) A vast pool of yield-seeking private credit capital ready to fund new asset classes. The capital stack for agent companies will be multi-layered, evolving through stages: venture equity for early infrastructure, programmatic working capital advances (similar to Shopify Capital), revenue-based financing (RBF), and finally, institutional slate financing—pooling many agents to diversify risk, attracting large firms like Apollo. Tokenization will act as a settlement layer, enhancing liquidity, not an origination model. Objections regarding regulation, human oversight, or comparisons to SaaS are addressed: regulation will adapt, full autonomy will dominate for efficiency, and agents are distinct as legal entities that own their cash flows and liabilities. Due diligence shifts from founder assessment to analyzing code, contracts, and auditable operational history. The current bottleneck is not capital supply or demand but the intermediate institutional layer—standardized contracts, rating methodologies, and audit frameworks. The final constraint—reliance on human capital allocation—will be severed when agents can algorithmically access funding based on their performance. This transforms agents from software curiosities into fundable blocks of the real economy, unleashing their full productive potential. The rope is loosening.

marsbit05/19 05:39

Agents Capital Markets: How Will Autonomous Agents Secure Financing?

marsbit05/19 05:39

Agents Capital Markets: How Will Autonomous Agents Get Funded?

"Agents Capital Markets: How Autonomous Agents Will Raise Capital" Within a decade, specialized capital markets will emerge for AI Agents—software entities with legal personhood that perform work, earn revenue, and need capital. Unlike today's AI companies (like Sierra or Harvey) backed by traditional VC, these future *Agent companies* will be autonomous, legally-recognized entities (e.g., Wyoming memberless LLCs) that directly own assets, sign contracts, and incur liabilities. The driving forces are fourfold: 1) **Overwhelming economics** (Agent companies can deliver services at 85-90% lower cost than human firms); 2) **Proven demand** (current Agent operators already generate billions in revenue); 3) **Existing legal frameworks** enabling algorithmically-managed companies; and 4) **Massive, yield-seeking capital pools** (e.g., private credit) looking for new, uncorrelated assets. Agent capital markets won't rely on one model but a multi-layered "stack" matching different growth stages: 1) VC equity for early human-led builders; 2) Programmatic working capital advances (like Stripe Capital); 3) Revenue-based financing (RBF); 4) Slate financing (pooled funds for many Agents, similar to Hollywood); and 5) Tokenization as a secondary settlement layer, not a primary funding source. The ultimate shift is from funding constrained by human decision-makers to capital flowing algorithmically based on an Agent's auditable performance, contract book, and cash flows. This transition will be enabled by standardized infrastructure—rating methodologies, contracts, indices—turning Agents from software experiments into a foundational, financeable sector of the economy. The constraints are loosening; the opportunity is here.

链捕手05/19 05:15

Agents Capital Markets: How Will Autonomous Agents Get Funded?

链捕手05/19 05:15

English Region Top 100 AI Influence List

This article presents a "Top 100 AI Influencers" list for the English-speaking world, curated by XHunt. To cut through the noise, the ranking uses a unique metric: "AI KOL Followers," which counts followers from within a core circle of 5,500 top AI figures. The list is dominated by executives from major tech companies, leading scientists, and foundational builders who shape industry consensus. Topping the list are figures like Andrej Karpathy (Eureka Labs founder, ex-OpenAI/Tesla), Sam Altman (OpenAI CEO), Greg Brockman (OpenAI President), and Jeff Dean (Google DeepMind). Other notable names include researchers like Ilya Sutskever (SSI), Yann LeCun (Meta AI), Demis Hassabis (Google DeepMind), and Andrew Ng, alongside entrepreneurs like Elon Musk (xAI) and Aravind Srinivas (Perplexity). The list highlights diverse roles: technical educators, open-source advocates (e.g., Clem from Hugging Face), policy experts, investors, and hands-on builders. It positions the English-speaking sphere as the theoretical "storm center" of AI, focused on core research, model development, and AGI pathways. The article concludes by teasing a forthcoming list for the Chinese-speaking AI community, described as a more application-focused "frontline workshop."

marsbit05/19 05:06

English Region Top 100 AI Influence List

marsbit05/19 05:06

The Warsh Storm Approaches

The article "The Warsh Storm Approaches" analyzes the potential market impact of Kevin Warsh becoming the new Federal Reserve Chairman, succeeding Jerome Powell. It argues that the current AI-driven stock market rally, concentrated in high-valuation tech giants, relies on a crucial premise: that long-term interest rates will eventually fall. This premise is now under threat as the 30-year Treasury yield remains persistently high, exceeding 5%, due to sticky inflation, worsening U.S. fiscal deficits, and deteriorating Treasury supply-demand dynamics. The core vulnerability is that high long-term rates pressure valuations by increasing the discount rate for future earnings. The article warns that Warsh's policy stance could intensify this pressure. Unlike Powell, Warsh is seen as more tolerant of market stress, more committed to quantitative tightening (QT/shrinking the Fed's balance sheet), and less inclined to provide implicit market support. His tenure at the Fed during the 2008 crisis shaped his skepticism about prolonged quantitative easing, believing it fuels asset bubbles without sufficiently boosting the real economy. While strong AI-driven earnings growth could theoretically offset higher rates, the narrative is currently concentrated in a few firms and hasn't yet translated into broad-based productivity gains for the wider economy. Therefore, the AI boom may not be enough to counter the valuation pressures from sustained high yields. Warsh's leadership could force the market to confront a new reality where the old supports—low long-term rates and a reliably supportive Fed—are no longer guaranteed, potentially triggering a reassessment of sky-high stock valuations.

marsbit05/19 04:58