# 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.

TechFlow Intelligence: Trump-Linked Companies Transfer $12 Million in Assets Before China Visit, 'The Big Short' Protagonist Warns of Stock Market Bubble Again

The article reports multiple developments across tech, crypto, and finance. In AI, Mozilla used AI for large-scale code review, Google confirmed hackers used AI to find zero-day exploits, and OpenAI deployed GPT-5.5 to find errors in math benchmarks. A court ruled Anthropic's scanning and destroying books for AI training as fair use, while its Claude platform launched on AWS. Google's new video model 'Omni' was leaked. In crypto/Web3, Trump-linked companies transferred $12M in crypto assets before a China visit. BlackRock chose Ethereum for tokenized funds, and a hacker stole $174k via a malicious NFT that tricked an AI. Jack Dorsey's first tweet NFT plummeted from $2.9M to under $5. In chips/hardware, TSMC approved an additional $20B for its Arizona plant. Apple's Tim Cook and Elon Musk will accompany Trump to China, while Nvidia's Jensen Huang is notably absent. For markets, Michael Burry warned of parabolic stock rises and suggested near-total sell-offs, with online discussions comparing current sentiment to the 1999 bubble. Other notes include WTI oil surpassing $100, a 20% price hike for Beijing-Shanghai high-speed rail, and new products like Unitree's $26.9k humanoid robot. The underlying theme suggests AI is becoming infrastructure, creating pressure on old systems while a new order is not yet ready, leaving investors anxious.

marsbit05/12 12:52

TechFlow Intelligence: Trump-Linked Companies Transfer $12 Million in Assets Before China Visit, 'The Big Short' Protagonist Warns of Stock Market Bubble Again

marsbit05/12 12:52

Splashing Out 27 Billion Yuan, OpenAI Establishes New Company to Accelerate AI Deployment

On May 11th, OpenAI announced the formation of a new company, "OpenAI Deployment Company," with an initial investment of over $4 billion (approximately 27.2 billion RMB). This venture aims to help businesses build and deploy AI solutions. OpenAI is also acquiring the AI consulting firm Toromo to rapidly scale the deployment company's capabilities. This new entity, majority-owned by OpenAI, brings together 19 investment, consulting, and system integration partners, led by TPG with co-lead founding partners including Advent International, Bain Capital, and Brookfield. OpenAI's Chief Revenue Officer, Denise Dresser, stated that while AI is becoming increasingly capable, the current challenge lies in integrating these systems into core business infrastructure and workflows. The deployment company is designed to bridge this gap and translate AI capabilities into operational impact. This move comes as OpenAI emphasizes the next competitive phase will depend on the efficiency of deploying AI in real business scenarios. The company reports over 1 million businesses already use its products and APIs. OpenAI is significantly increasing its investments in computing power, with co-founder Greg Brockman stating the company expects to spend $50 billion on compute this year, a dramatic increase from $3 million in 2017. The announcement follows OpenAI's recent completion of a record $122 billion funding round in late March, led by Amazon, Nvidia, and SoftBank, valuing the company at $852 billion post-money. Major strategic investors committed $110 billion as a base for this round. Concurrently, OpenAI is advancing its core model development. It has shifted focus from its Sora video generator to developing advanced robotics and AI models that interact with the physical world. It has also begun allowing select users access to a new model specialized in identifying software vulnerabilities and is reportedly preparing to launch an enhanced image generation model in the coming weeks. According to reports citing founder Sam Altman, OpenAI is considering an IPO as early as 2027, with a potential valuation around $1 trillion.

marsbit05/12 11:40

Splashing Out 27 Billion Yuan, OpenAI Establishes New Company to Accelerate AI Deployment

marsbit05/12 11:40

The Essence of AI Layoffs: Why More AI Adoption Leads to More Corporate Anxiety?

The author, awaiting potential inclusion on an 8000-person layoff list, analyzes the true nature of recent "AI-driven" layoffs. They argue that while AI use, particularly tools like Claude for code generation, has skyrocketed and boosted developer output (e.g., 2-5x more code commits), this has not translated into proportional business growth or revenue. The core issue is a misalignment between increased "Input" (code) and tangible "Outcomes" (user value, revenue). AI acts as a costly B2B SaaS, inflating operational expenses without guaranteed returns. Two key problems emerge: 1) The friction that once filtered out bad ideas is gone, as AI allows cheap pursuit of even weak concepts. 2) Organizational "alignment tax"—the difficulty of coordinating across teams—becomes crippling when development velocity outpaces consensus-building. Thus, layoffs serve two immediate purposes: 1) To offset ballooning AI costs (Token consumption) and maintain cash flow, as rising input costs without outcome growth destroys unit economics. 2) To reduce organizational bloat and alignment friction by simply removing teams, thereby speeding up execution in the short term. Therefore, these layoffs are fundamentally caused by AI, even if AI doesn't directly replace roles. They represent a painful correction until companies learn to convert AI-driven productivity into real business outcomes and streamline organizational coordination to match the new pace of work. The cycle will continue until this learning curve is mastered.

marsbit05/12 10:23

The Essence of AI Layoffs: Why More AI Adoption Leads to More Corporate Anxiety?

marsbit05/12 10:23

How to Automate Any Workflow with Claude Skills (Complete Tutorial)

This is a comprehensive guide to mastering Claude Skills, a feature for creating permanent, reusable instruction sets that automate specific workflows. Unlike simple saved prompts, Skills function like trained employees, delivering consistent, high-quality outputs by defining the entire task process, standards, error handling, and output format. The guide is structured in four phases: **Phase 1: Installation (5 minutes).** Skills are folders containing a `SKILL.md` file. The user is instructed to find a relevant Skill online, install it, test it on a real task, and compare its performance to one-off prompts. **Phase 2: Building Your First Custom Skill.** Start by rigorously defining the Skill's purpose, trigger phrases, and providing a concrete example of perfect output. The `SKILL.md` file has two parts: a YAML frontmatter with a specific name/description/triggers, and a detailed, step-by-step workflow written in natural language with examples and quality standards. **Phase 3: Testing & Optimization for Production.** Test the Skill in three scenarios: 1) a standard, common task; 2) edge cases with missing or conflicting data; and 3) a pressure test with maximum complexity. Any failure indicates a needed instruction. Implement a weekly optimization cycle to continuously refine the Skill based on real usage. **Phase 4: Building a Complete Skill Library.** The goal is to create a team of Skills for all repetitive tasks. Examples are given for industries like real estate, marketing, finance, consulting, and e-commerce. The user should list their tasks, prioritize them, and build one new Skill per week, maintaining a master document to track their library. The conclusion emphasizes the compounding time savings: ten Skills saving 30 minutes each per week reclaims over 260 hours (6.5 work weeks) per year, fundamentally transforming one's work system.

marsbit05/12 09:45

How to Automate Any Workflow with Claude Skills (Complete Tutorial)

marsbit05/12 09:45

Dialogue with Vitalik, Xiao Feng, Aya Miyaguchi, and Joseph Chalom: From the 'Subtraction Principle' to the Agent Economy

Conversation with Vitalik Buterin, Xiao Feng, Aya Miyaguchi, and Joseph Chalom: Highlights from the Ethereum Application Summit on key future directions. Vitalik Buterin discussed the concept of "Full Stack Open Source Security," extending security from the protocol to hardware layers like wallets and chips. He predicted AI will simplify blockchain interaction, enabling natural language commands for complex operations. He emphasized that Ethereum's future focus should be on security, decentralization, and trustless infrastructure—the areas where it holds its core competitive edge. The fusion of AI, Fully Homomorphic Encryption (FHE), and blockchain is seen as crucial for real-world applications requiring privacy, such as healthcare. Xiao Feng underscored the importance of simplifying technology for mass adoption. He drew parallels to the evolution from command lines to GUIs and apps, suggesting that AI-driven natural language interfaces will be key to bringing more users into Web3. He stressed that while performance is important, Ethereum must continue to uphold its foundational principles of decentralization and user sovereignty. Aya Miyaguchi, Chair of the Ethereum Foundation, explained the evolving role of the Foundation through the "Principle of Subtraction." As the ecosystem matures, the EF is stepping back from areas where the community can take the lead, acting as one of many "gardeners" rather than a central driver. She highlighted that real applications are built on Ethereum's core values: censorship resistance, open source, security, and privacy. The concept of "Local-first" initiatives, like the Ethereum Applications Guild (EAG), was also emphasized for leveraging regional strengths to create global impact. Joseph Chalom, CEO of SharpLink, positioned Ethereum as the future infrastructure for global capital markets, differentiating it from Bitcoin through its "productivity" via staking yields. He envisioned the rise of an "Agent Economy" by 2027, where AI agents, powered by Web3 wallets, will autonomously manage financial tasks like yield optimization and RWA investments. The summit concluded that with core infrastructure maturing, the application layer is now the key driver for Ethereum's next phase of growth and real-world adoption.

marsbit05/12 09:43

Dialogue with Vitalik, Xiao Feng, Aya Miyaguchi, and Joseph Chalom: From the 'Subtraction Principle' to the Agent Economy

marsbit05/12 09:43

Cerebras IPO: A $48.8 Billion Valuation—Is the 'Nvidia Challenger' a Bubble or a New King?

Cerebras Systems, positioning itself as an NVIDIA challenger, is going public with a $48.8 billion valuation despite several underlying paradoxes revealed in its S-1 filing. While 2025 revenue grew 76% to $510M and GAAP net income was $237.8M, this profitability relies heavily on a one-time, non-cash accounting gain. Adjusting for this, the company's non-GAAP net loss actually widened to $75.7M. Furthermore, customer concentration remains extreme: 86% of 2025 revenue came from two Abu Dhabi-based entities, MBZUAI (62%) and G42 (24%). Its landmark deal with OpenAI, valued at over $20 billion, creates a complex, nested relationship where OpenAI is simultaneously a major customer, lender, warrant holder, and strategic partner with exclusivity clauses. Cerebras's technical edge in latency-sensitive AI inference is real, with its wafer-scale chip outperforming competitors in benchmarks. However, this advantage is confined to a specific niche, not the broader AI training market dominated by NVIDIA's CUDA ecosystem. With a 95x price-to-sales ratio, the valuation demands flawless execution of the OpenAI contract and massive future revenue growth. Key long-term risks include intense competition from giants like NVIDIA and AMD, a dual-class share structure granting insiders near-total voting control, and ongoing geopolitical uncertainties regarding export controls. The IPO is a pivotal capital markets event for AI infrastructure. As an investment, it represents a high-risk, high-reward bet on the "inference-first" narrative and Cerebras's ability to dominate its specialized segment, underpinned by a valuation that highlights the current fervor in the sector.

marsbit05/12 09:05

Cerebras IPO: A $48.8 Billion Valuation—Is the 'Nvidia Challenger' a Bubble or a New King?

marsbit05/12 09:05

The AI Investment Landscape Is Being Reshaped: Beyond the 'Magnificent Seven', What Opportunities Lie in the Semiconductor Supply Chain?

AI Investment Map is Reshaping: Opportunities Beyond the 'Magnificent Seven' Since ChatGPT ignited the AI wave, investment initially focused on the "Magnificent Seven" tech giants dominating cloud infrastructure. However, the rise of DeepSeek and debates on AI capital expenditure effectiveness are shifting this dynamic. Investors now recognize opportunities deeper in the supply chain—the companies providing the essential "picks and shovels." Early concerns about an AI investment "arms race" and potential low returns were partly alleviated by strong Q1 earnings from cloud providers, validating robust compute demand. This has highlighted a more certain investment thesis: regardless of which AI applications ultimately win, massive capital expenditure will first fuel demand for semiconductors and related components. This "pick-and-shovel" logic has driven semiconductor ETFs to record highs. Key beneficiaries include: * **Memory Chipmakers (e.g., SK Hynix, Samsung, Micron)**: High Bandwidth Memory (HBM) is a critical bottleneck for AI training. * **Photonics Companies**: Crucial for high-speed data transfer within AI data centers. * **The Broader "AI-11" Semiconductor Ecosystem**: This encompasses foundries & lithography (TSMC, ASML), logic & custom chips (AMD, Broadcom, Intel, Marvell), and enterprise storage (SanDisk, Western Digital). Every dollar of AI infrastructure spending flows through this chain. While the "Magnificent Seven" remain dominant in market size, their earnings growth premium over the rest of the S&P 500 ("S&P 493") is narrowing. Market attention and marginal investment are shifting towards the expanding semiconductor supply chain. The investment narrative is evolving from "betting on the ultimate AI winner" to "investing in the certainty of the infrastructure build-out." Understanding this shift from the demand side to the supply side is key to identifying future AI investment opportunities.

marsbit05/12 08:06

The AI Investment Landscape Is Being Reshaped: Beyond the 'Magnificent Seven', What Opportunities Lie in the Semiconductor Supply Chain?

marsbit05/12 08:06

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