# Сопутствующие статьи по теме Supply Chain

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

Rejecting Nvidia's Offer at $6 Per Share, He Says He Can Earn More Trading Stocks

A mysterious X user known as Serenity (@aleabitoreddit) has become a significant influence in financial markets, with over 150,000 followers across three continents tracking his stock picks. His recent endorsement of UK hardware company Raspberry Pi ($RPI) triggered an approximate 90% surge in its stock price over two days, later validated by strong earnings that matched his forecasts. Previously banned from Reddit's r/wallstreetbets for a controversial post, Serenity moved to X. He claims to be a former AI research scientist who declined a job offer from Nvidia when its stock was around $6. His identity and reported investment returns (e.g., 630% in one year) remain unverified. Serenity's strategy focuses on identifying "chokepoints"—critical, often overlooked components in complex supply chains, like the indium phosphide substrates for AI datacenter optics. His calls on small-cap stocks such as AXTI and Sivers Semiconductors (SIVE) have preceded major price rallies driven by events like export controls or strategic partnerships. While his free, public research differs from typical financial influencers who sell courses, the anonymous, self-reported nature of his success warrants caution. He represents a new phenomenon where detailed, niche analysis can move markets, but his track record lacks independent audit.

marsbit05/22 02:59

Rejecting Nvidia's Offer at $6 Per Share, He Says He Can Earn More Trading Stocks

marsbit05/22 02:59

The First Large-Scale Strike in the AI Era Comes from the Factories That Build AI

The article describes a potential large-scale strike at Samsung Electronics, narrowly averted in May 2026 after a temporary agreement. The strike, planned by the company's union, would have been the first major labor action in the AI era targeting a core AI supply chain player. Samsung, alongside SK Hynix, produces roughly two-thirds of the world's memory chips, critical components for AI training and data centers like HBM. An 18-day strike could have disrupted global supply, affecting prices and production for tech companies and cloud providers. For South Korea, where semiconductors constitute about 35% of exports and Samsung represents a quarter of the stock market's value, such an action threatens national economic stability. The union's demands include a 7% base wage increase and, crucially, a clear, substantial profit-sharing model. They want 15% of annual operating profit as an employee bonus pool and the removal of the existing cap (about 50% of annual salary). This frustration is amplified by seeing rival SK Hynix successfully negotiate a deal granting employees 10% of operating profit as bonuses, with reports suggesting some workers could receive bonuses equivalent to hundreds of thousands of dollars. The conflict stems from deeper issues in South Korea's chaebol (conglomerate) system, where rapid national industrialization often prioritized corporate growth over labor rights. Samsung long maintained a "no union" policy until a 2020 apology from its leader. The article argues this strike highlights a fundamental tension in the AI age: as technology advances and corporate profits soar—often driven by AI—the workers who build the infrastructure are demanding a fair share and dignity, rejecting the notion that they are mere expendable components in a machine that "must not stop." The piece concludes that the true test of the AI era isn't just computational power, but whether the people who build the future can secure a stable and valued place within it.

marsbit05/21 05:16

The First Large-Scale Strike in the AI Era Comes from the Factories That Build AI

marsbit05/21 05:16

KUN and Pharos Network Forge Strategic Partnership to Jointly Drive Innovation in RealFi, RWA, and Cross-Border Payment Infrastructure

Hong Kong. Layer 1 infrastructure Pharos Network and licensed digital payment expert KUN have signed a strategic MoU. They will integrate Pharos's institutional blockchain with KUN's licensed global payment rails to drive the tokenization of supply chain credit assets and enable more efficient global settlement on-chain. **Background:** Emerging market SMEs face severe working capital challenges due to slow, costly traditional trade finance, often waiting 30-90 days for payment after delivery. While RWA tokenization is a focus, few projects effectively connect underlying infrastructure to real commerce and licensed payment networks. **Collaboration Focus:** The partnership aims to bridge this gap by bringing supply chain credit and B2B cross-border payments on-chain compliantly. Initial priorities include: * Tokenizing supply chain credit assets to unlock liquidity. * Enabling native on-chain settlement of digital assets. * Exploring enterprise virtual card solutions. * Providing compliant on-chain financial services for verticals like commodities, trade, B2B e-commerce, and Web3. **Executive Quotes:** * Wish Wu, Co-founder & CEO of Pharos Network, highlighted KUN's trusted, licensed payment network as a perfect fit for bringing supply chain assets and cross-border capital flows on-chain accessibly. * Dr. Louis Liu, Founder & CEO of KUN, stated that settlement certainty is RealFi's final hurdle. Bridging KUN's payment rails with Pharos's infrastructure will help convert on-chain assets into real-world liquidity with institutional-grade trust. They will also explore AI-driven optimization for global capital flows. Pharos mainnet is live with over 50 dApps. This partnership strengthens its position as RealFi infrastructure by linking licensed payment systems with on-chain finance.

marsbit05/20 13:35

KUN and Pharos Network Forge Strategic Partnership to Jointly Drive Innovation in RealFi, RWA, and Cross-Border Payment Infrastructure

marsbit05/20 13:35

Making AI Products Is No Longer the Hard Part; Being Seen Is: Developers, Web3, and Chinese AI Opportunities at mu Shanghai

The article discusses the shifting challenges of AI entrepreneurship, based on insights from the mu Shanghai AI WEEK event in May 2026. As AI tools drastically lower the barrier to creating product prototypes, the core difficulty for startups has moved from "how to build" to "who to build for"—finding real users, sustainable business models, and community engagement. The event itself was structured as an extended, immersive developer community space rather than a traditional conference, attracting a global mix of participants (40% AI, 20-30% Web3). This format emphasized deep networking and collaborative creation over one-way presentations. A key observation is that with powerful models and coding assistants becoming ubiquitous, execution is less of a moat. The new scarce resource is judgment—identifying valuable, defensible scenarios where an application won't be quickly rendered obsolete by the next model update. This pushes competition downstream to distribution, user acquisition, and commercialization. Notably, many Web3 practitioners are migrating into AI, bringing with them expertise in community building, global collaboration, and grassroots marketing—skills highly relevant as AI apps fight for visibility. Meanwhile, opportunities in AI hardware, robotics, and embodied intelligence are seen as more durable, leveraging China's robust manufacturing and supply chain ecosystem as a key advantage. The article notes that major Chinese model companies (like MiniMax) are now actively competing for developer mindshare through community programs, hackathons, and improved tooling, recognizing developers as core users. Ultimately, the conclusion is that while AI simplifies building, the harder part of the journey is ensuring a product is truly needed, understood, and retained by its users.

marsbit05/19 07:51

Making AI Products Is No Longer the Hard Part; Being Seen Is: Developers, Web3, and Chinese AI Opportunities at mu Shanghai

marsbit05/19 07:51

Farewell to the Copper Era: Understanding the Logic of the AI Silicon Photonics Industry Chain and Key US Stock Players

**Summary: The Era of Silicon Photonics and Key AI Infrastructure Stocks** The article delves into the transition from copper-based interconnects to silicon photonics (SiPh) as a critical enabler for next-generation AI data centers. It explains that copper faces fundamental physical limits—the bandwidth wall, density wall, and power wall—at high data rates (1.6T+), making a material shift essential. Silicon photonics, which integrates components like lasers, modulators, and detectors onto a silicon chip, offers a solution by leveraging mature CMOS manufacturing for cost-effective, high-volume production. A key challenge is that silicon itself is not an efficient light source, making Indium Phosphide (InP) lasers a critical and supply-constrained component. A major industry catalyst was NVIDIA's 2025 GTC announcement, declaring optical interconnects a "standard" from its Rubin platform onward, followed by strategic investments to secure the supply chain. The industry is structured in four key layers: 1. **Foundries:** TSMC leads with its COUPE platform, while Tower Semiconductor (specialized SiPh foundry) and GlobalFoundries are major players. 2. **Core Component Suppliers:** Lumentum is highlighted as the sole volume manufacturer of the crucial 200G/lane EML laser, with orders locked by NVIDIA through 2027. 3. **Module & System Manufacturers:** Coherent holds significant market share, with Chinese manufacturers like InnoLight also noted for scale. 4. **System Integrators:** NVIDIA, Broadcom, and Marvell dominate this layer, setting standards and integrating technology. The article identifies core public investment targets: **NVIDIA (NVDA)** as the ecosystem driver; **Broadcom (AVGO)** and **Marvell (MRVL)** in networking/switching chips; **Lumentum (LITE)** and **Coherent (COHR)** for critical components; and foundries **TSMC (TSM)** and **Tower Semiconductor (TSEM)**. Private companies Lightmatter and Ayar Labs are noted as key IPO candidates. The silicon photonics shift is driving a re-rating of company valuations, moving them from traditional telecom/industrial metrics to premium AI infrastructure multiples. The industry features high barriers to entry (e.g., multi-year lead times for InP laser capacity, complex 3D integration/thermal management, and lengthy customer qualification cycles), suggesting a "winner-takes-most" dynamic. Risks include dependence on hyperscaler capex cycles, potential technology disruption among competing optical approaches (LPO, CPO, OCS, Optical I/O), and a timeline where widespread CPO deployment may not occur until ~2028, with LPO serving as a transitional technology. The conclusion advises that betting on the overall industry trend may be safer than betting on any single company.

marsbit05/19 02:15

Farewell to the Copper Era: Understanding the Logic of the AI Silicon Photonics Industry Chain and Key US Stock Players

marsbit05/19 02:15

Physical AI is Hot, Some New Thoughts from Me

The term "Physical AI" is gaining significant traction, marking a shift from AI that processes information to AI that understands and interacts with the physical world. Unlike traditional AI confined to screens, Physical AI involves integrating intelligence into robotic bodies to perform tasks in environments governed by gravity, friction, and inertia. The concept, formally defined in a 2020 paper, focuses on creating embodied systems that can complete perception-to-action cycles. 2026 is identified as a pivotal "deployment year," where the focus moves from demonstrations to practical utility. Companies like China's Zhiyuan Robotics have transitioned to live, unscripted factory deployments and announced mass production targets. Internationally, Figure AI, after a major funding round, shifted to its own neural system, while NVIDIA partnered with major industrial robot firms to upgrade millions of existing units with AI capabilities. A key trend is the crossover from the automotive supply chain. Companies like Aptiv and Valeo are entering the Physical AI space, leveraging their expertise in sensors, control systems, and mass production from the autonomous vehicle sector. This "technology spillover" is accelerating development, as seen with Tesla's plans to repurpose automotive production lines for its Optimus robot. The technical breakthrough enabling this progress is the engineering maturity of "world models." Previously theoretical, these AI models can now simulate physical interactions and generate vast, realistic synthetic training data for robots. Innovations from NVIDIA's Cosmos, Ant's LingBot-World, and others have made this capability more accessible, drastically reducing the cost and time needed for real-world data collection. This is driving a fundamental architectural shift in robotics: from the traditional "sense-plan-act" model, reliant on pre-programmed rules, to a "sense-reason-act" paradigm where neural networks reason and make decisions. This change represents a new paradigm where machines understand the world's physics. The competition is intense, with the landscape still forming. While the direction is clear, success will depend not just on AI algorithms but on manufacturing scalability, supply chain resilience, and efficient data strategies, with infrastructure providers potentially capturing significant value in this new era.

marsbit05/18 04:43

Physical AI is Hot, Some New Thoughts from Me

marsbit05/18 04:43

This Chip Sector Is on Fire

The global AI chip market is undergoing a significant paradigm shift, with ASICs (Application-Specific Integrated Circuits) emerging from a niche to a mainstream force, challenging the long-held dominance of GPUs in AI training. This "golden era" for ASICs is primarily driven by the industry's pivot from training to inference, where the cost and energy efficiency advantages of custom chips become critical for scaling to billions of users. Key signals include Google's TPU capturing 78% of its AI server shipments in Q1 2026, OpenAI's plans for a massive custom ASIC cluster with Broadcom, and cloud providers (CSPs) increasingly favoring in-house or custom designs for supply chain control and cost efficiency. Market forecasts are bullish: AI ASIC revenue is projected to hit $300 billion by 2027, with a 34% CAGR, potentially reaching a 45% share of the AI chip market. The competitive landscape is expanding beyond traditional leaders Broadcom and Marvell. MediaTek is aggressively targeting the data center ASIC market, projecting over $10 billion in 2026 revenue, while Qualcomm, leveraging its AlphaWave acquisition, is launching customized inference chips. These mobile chip giants are leveraging their SoC design expertise for a cloud-side transition. In China, companies like VeriSilicon and ASR Microelectronics are capitalizing on this trend as pivotal "enablers," providing full-stack ASIC design services and experiencing explosive order growth, particularly for cloud-side AI projects. However, challenges remain: high development costs, software ecosystem gaps compared to NVIDIA's CUDA, dependency on advanced packaging capacity (like TSMC's CoWoS), and the fundamental trade-off between customization and flexibility. The future is not a simple replacement of GPUs by ASICs but a more specialized coexistence. The consensus points toward "GPUs for training, ASICs for inference," or hybrid clusters. Ultimately, the rise of ASICs represents a democratization of computing power, shifting definition authority from a single chip giant to a broader ecosystem of cloud providers and end-users, offering the industry more choice in the silicon that powers AI.

marsbit05/18 00:29

marsbit05/18 00:29

The Semiconductor Century: Investment Roadmap Amidst the 2026 AI Surge

The Semiconductor Century: Investment Roadmap in the 2026 AI Surge This analysis outlines the pivotal role of semiconductors in the 2026 AI-driven landscape. With the global semiconductor market projected to reach ~$9.75 trillion in 2026, AI infrastructure spending by hyperscalers is a primary growth driver, fundamentally shifting demand from consumer electronics to strategic technology assets. The report breaks down the industry into four key segments: 1) Designers (e.g., Nvidia, AMD) who own high-margin IP; 2) Foundries, led by TSMC which manufactures ~90% of the world's most advanced chips; 3) Equipment makers like ASML, the sole producer of critical EUV lithography machines; and 4) Memory specialists such as SK Hynix, crucial for supplying high-bandwidth memory (HBM) for AI servers. It highlights significant companies: Nvidia (dominant in AI GPUs and CUDA software), TSMC (critical but geopolitically concentrated foundry), ASML (monopoly in advanced lithography), AMD (key alternative to Nvidia), Broadcom (leader in custom AI chips), and SK Hynix (leading HBM supplier). For diversified exposure, semiconductor ETFs like SMH, SOXX, and SOXQ are presented. Key investment risks are emphasized: over-reliance on AI demand, acute geopolitical and supply chain concentration in Taiwan, policy uncertainty around export controls, the cyclical nature of memory markets, and high valuations for leaders like Nvidia and Broadcom. Critical 2026 catalysts include the industry's push toward a $1 trillion annual sales milestone, the ramp-up of TSMC's Arizona factory, the deployment of Nvidia's next-generation Vera Rubin platform, AMD's market share progress, and HBM4 supply dynamics. The conclusion advises investors to balance the sector's extraordinary growth against its very real risks—geopolitical concentration, AI dependency, memory cyclicality, and valuation—to make informed decisions.

marsbit05/14 10:40

The Semiconductor Century: Investment Roadmap Amidst the 2026 AI Surge

marsbit05/14 10:40

Trump's Visit to China: Which CEOs Were on Air Force One

Eight American CEOs accompanied former President Trump on his visit to Beijing. The delegation included Elon Musk (Tesla), Tim Cook (Apple), Jensen Huang (NVIDIA), Larry Fink (BlackRock), Kelly Ortberg (Boeing), David Solomon (Goldman Sachs), Stephen Schwarzman (Blackstone), and Jane Fraser (Citigroup), among others from Meta, Mastercard, and Visa. The article argues their collective presence highlights a profound structural dependence of top U.S. companies on the Chinese market. Key reasons for their attendance included: Tesla's reliance on its Shanghai gigafactory; Apple's unfinished supply chain transition from China; NVIDIA's need to resolve H200 chip export and tariff issues; BlackRock's pursuit of critical licenses; Boeing's hope for a landmark aircraft order; Goldman Sachs safeguarding its full-owned securities business; and Citigroup awaiting approval for a brokerage license. The analysis concludes that the ability of Beijing to convene this powerful group signals a shift in leverage, where access to China's market, supply chains, and regulatory approvals has become a strategic necessity, not merely an option, for major U.S. corporations.

marsbit05/14 04:59

Trump's Visit to China: Which CEOs Were on Air Force One

marsbit05/14 04:59

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?