# Сопутствующие статьи по теме Chips

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

Tom Lee's Core Investment Logic for 2026: Companies Selling Scarce Assets Are Crushing the Market

Tom Lee, founder of Fundstrat and manager of the Granny Shots fund, argues "scarcity" is the core investment theme for 2026. His thesis states companies selling "scarce assets"—products or services with structurally constrained supply and explosively growing demand—are dominating the market due to strong pricing power. He identifies three key scarcity areas: 1) AI compute (e.g., NVIDIA, AMD), limited by advanced chip manufacturing capacity; 2) AI memory/HBM (e.g., Micron), facing complex production challenges; and 3) energy infrastructure (e.g., GE Vernova), with long lead times for equipment needed to power booming data centers. Lee provides a macro trading framework: peaking oil prices signal lower inflation and potential Fed rate cuts, which benefit growth assets like the S&P 500 and Magnificent 7 stocks. Despite the S&P reaching his initial 7300 target, he sees a potential "feel-like-a-bear-market" mid-year pullback as a buying opportunity, raising his year-end target to 7700. Investment themes are prioritized as: 1) Global labor scarcity + AI automation, 2) Cybersecurity + energy security. The conclusion is that identifying companies benefiting from fundamental supply-demand imbalances, not just chasing rallies, is the path to outperformance in 2026.

链捕手17 ч. назад

Tom Lee's Core Investment Logic for 2026: Companies Selling Scarce Assets Are Crushing the Market

链捕手17 ч. назад

TechFlow Intelligence Briefing: S&P and Nasdaq Post Sixth Consecutive Weekly Gains; Aptos Foundation Bets $50 Million on AI Agent

TechFlow Intelligence Report Summary: The S&P and Nasdaq both recorded six consecutive weeks of gains. Aptos Foundation announced a $50 million investment in AI Agents, aiming to build infrastructure for AI and on-chain markets. Intel's stock hit a record high after a preliminary deal, reportedly brokered by the Trump administration, to manufacture chips for Apple. ByteDance plans to increase its AI infrastructure spending significantly to 200 billion yuan. Cloudflare's CEO stated AI made 1,100 roles obsolete while revenue hit a new high, sparking discussions on AI's impact on jobs. Strong US non-farm payroll data eased stagflation concerns, influencing market expectations for Fed policy. The report connects these developments to a broader narrative: a new power structure is forming around chips, computing power, and AI, involving governments, capital, and tech giants, while geopolitical tensions in the Strait of Hormuz highlight the parallel strain on energy security.

marsbitВчера 11:09

TechFlow Intelligence Briefing: S&P and Nasdaq Post Sixth Consecutive Weekly Gains; Aptos Foundation Bets $50 Million on AI Agent

marsbitВчера 11:09

The US Stock Market in 2026, It's Almost Too Easy, and That Makes Me Nervous

The U.S. stock market's performance in 2026, particularly in the semiconductor memory sector, has generated significant returns that make some investors uneasy. A popular sentiment contrasts the perceived skill required for success in China's A-shares with the apparent ease of profiting from simply holding U.S. stocks. The primary driver is a global memory chip boom. Stocks like Micron, Seagate, Western Digital, and especially SanDisk (spinning off from WDC in 2025) have skyrocketed, with some gains exceeding 500% or even 2200%. Korean giants Samsung and SK Hynix, dominating their domestic index, have also surged. This rally is fueled by an AI-driven demand surge for memory like HBM (High-Bandwidth Memory), critical for AI chips. Tech giants like Google and Microsoft are placing massive, "unpriced" orders, while analysts continuously upgrade forecasts. SK Hynix reported its 2026 HBM capacity is already sold out. Despite record profits and sky-high margins (e.g., SK Hynix's 72% operating margin), major memory manufacturers are deliberately restricting capital expenditure and capacity expansion, controlling over 90% of DRAM supply. This supply discipline sustains high prices but draws parallels to cartel behavior. The situation presents two narratives. The bullish case sees AI demand as a structural, long-term shift with a prolonged supply gap. The bearish case, exemplified by short-seller Citron's failed bet against SanDisk, warns of a classic commodity cycle where prices eventually crash rapidly, as seen historically. The irony is noted: while retail investors marvel at easy gains, insiders like Western Digital are selling SanDisk shares at a 25% discount. Ultimately, the high cost of memory in consumer devices feeds into the record profits of memory companies and the soaring stock prices, leading many to question the sustainability of a market where making money seems "as easy as breathing."

marsbit2 дня назад 02:57

The US Stock Market in 2026, It's Almost Too Easy, and That Makes Me Nervous

marsbit2 дня назад 02:57

AI Giants Enter the Dark Forest

In the AI industry's "dark forest," major players like Anthropic, OpenAI, and DeepSeek are strategically withholding their most advanced models to avoid becoming targets in a high-stakes competitive landscape. Anthropic released Claude Opus 4.7 but admitted it underperforms compared to their unreleased model Mythos, citing safety concerns. They delayed addressing user complaints about performance regression until OpenAI’s GPT-5.5 launch, highlighting a tactic of controlled disclosure aligned with competitors’ moves. OpenAI’s GPT-5.5, though a full retrain since GPT-4.5, was seen as incremental rather than revolutionary. Leaks revealed internal models like Glacier and Heisenberg, indicating significant unreleased capabilities. OpenAI acknowledges a "capability overhang," where real model power exceeds what users experience, often due to infrastructure-driven throttling. DeepSeek launched V4 Preview, a cost-efficient model, but its full potential (V4 Pro Max) awaits Huawei’s Ascend 950 super-nodes量产 in late 2026. Their strategy focuses on affordability and scalability, aiming to democratize AI access globally, a move noted even by NVIDIA’s CEO as a disruptive threat. Together, these actions reflect a broader trend: leading AI labs are deliberately pacing releases, hiding strengths, and aligning disclosures with competitive dynamics—each avoiding the risk of exposure in a forest where first movers become targets.

marsbit04/25 12:47

marsbit04/25 12:47

Not Just DeepSeek, Big Tech Companies Want to 'Abandon' NVIDIA

The article discusses how major tech companies are attempting to reduce their reliance on Nvidia, despite its dominant position in the AI chip market, where it enjoys a 75.2% GAAP gross margin. Companies like DeepSeek are adapting their models to run on domestic alternatives such as Huawei’s Ascend chips, while in the U.S., Google and Meta are developing their own AI chips (TPU and MTIA series) to complement external partnerships. Nvidia’s CEO, Jensen Huang, acknowledges that Moore’s Law is fading and that export restrictions may slow China’s AI development but could ultimately spur a self-sufficient ecosystem. Notably, Chinese firms are leading in open-source models, which could eventually challenge Nvidia’s monopoly. OpenAI is actively diversifying away from Nvidia, signing a $20 billion deal with Cerebras, a startup using wafer-scale chips to reduce latency and cost. Cerebras, founded by Andrew Feldman, aims to challenge Nvidia with its unique architecture but faces financial and competitive risks, including heavy dependence on OpenAI and geopolitical tensions. While competition is intensifying with players like AMD and Groq (which partnered with Nvidia), the overall demand for compute continues to grow. The market is shifting toward a diversified supplier model, though Nvidia remains a formidable force.

marsbit04/24 12:29

Not Just DeepSeek, Big Tech Companies Want to 'Abandon' NVIDIA

marsbit04/24 12:29

Chip Stocks Hit Record Highs Since 2000, SaaS Stocks Fall to Yearly Lows: Two Worlds Under the AI Divide

The article highlights a stark divergence in the tech market driven by AI: semiconductor stocks are surging while SaaS stocks plummet. On April 23, Texas Instruments saw its best single-day performance since 2000, with Q1 revenue up 19% and data center revenue surging 90%. Intel also reported blowout results, with revenue far exceeding expectations and its stock rising over 20% after hours. The semiconductor ETF (SMH) is up nearly 28% this year. In contrast, SaaS companies like ServiceNow and IBM experienced historic declines, with ServiceNow dropping 18% despite beating earnings estimates. The software ETF (IGV) has entered a technical bear market, losing about $2 trillion in market cap. The trend, dubbed "SaaSpocalypse," reflects fears that AI agents could reduce the need for software licenses and enable companies to build internal tools instead of relying on SaaS subscriptions. The market is rotating capital from software to semiconductors, betting on AI infrastructure as a safer play. Chip stocks now trade at high valuations—Texas Instruments at a P/E over 50, Intel at 120 times forward earnings—pricing in years of growth. However, this depends on continued massive AI capital expenditure from tech giants. If spending slows, the trend could reverse. For now, the divide persists: chips celebrate, SaaS struggles.

marsbit04/24 07:59

Chip Stocks Hit Record Highs Since 2000, SaaS Stocks Fall to Yearly Lows: Two Worlds Under the AI Divide

marsbit04/24 07:59

China's Chips, the Hidden Intersection of DeepSeek and Kimi

China's AI startup Kimi has released its latest open-source model, K2.6, featuring significant improvements in coding and agent capabilities. This version enhances long-context processing, supports up to 4,000 lines of code generation, and improves API accuracy and multi-agent collaboration, allowing up to 300 sub-agents to work in parallel. Kimi also introduced architectural innovations like MuonClip, Kimi Linear, and Attention Residuals to optimize scaling and efficiency. A key development is the "Prefill-as-a-Service" (PrfaaS) approach, which decouples prefill and decode tasks across data centers using heterogeneous hardware, significantly reducing latency and cost per token. This strategy not only improves performance but also creates opportunities for integration with domestic AI chips, as reliance on Chinese hardware grows due to export restrictions. Both Kimi and DeepSeek are increasingly aligning with local semiconductor advancements, shaping a new ecosystem for AI development in China.

marsbit04/22 06:58

China's Chips, the Hidden Intersection of DeepSeek and Kimi

marsbit04/22 06:58

Jensen Huang Publicly Challenges Google and Amazon, Is the Chip Business Entirely Sustained by Anthropic?

In a candid interview, Nvidia CEO Jensen Huang challenged competitors like Google and Amazon, admitted past strategic errors, and criticized U.S. export controls on AI chips. He framed Nvidia’s role as turning “electricity in, tokens out,” emphasizing the complexity and value of AI inference. Huang dismissed rival custom chips like Google’s TPU and Amazon’s Trainium as inflexible and niche, claiming their growth relies heavily on clients like Anthropic. He also acknowledged missing early investment opportunities in OpenAI and Anthropic. On China, Huang warned that export restrictions risk pushing the country toward self-sufficiency and could cost U.S. leadership in AI. Finally, he explained Nvidia’s acquisition of Groq as a move to serve premium, low-latency token markets. Throughout, Huang emphasized ecosystem trust and Nvidia’s central role in global AI infrastructure.

marsbit04/21 07:00

Jensen Huang Publicly Challenges Google and Amazon, Is the Chip Business Entirely Sustained by Anthropic?

marsbit04/21 07:00

Amazon Invests Additional $25 Billion in Anthropic, AI Infrastructure 'Arms Race' Escalates

Amazon announces an additional investment of up to $25 billion in Anthropic, with $5 billion delivered immediately and the remaining contingent on performance milestones. This follows a recent $50 billion investment in OpenAI, highlighting Amazon's strategy of backing leading AI labs. The deal includes a commitment from Anthropic to spend over $100 billion on AWS infrastructure over the next decade, securing up to 5 gigawatts of computing power to address growing demand and capacity constraints. Anthropic’s annualized revenue has surpassed $30 billion, but the company faces infrastructure strain due to rapid user growth. The investment will support scaling Claude’s capabilities using Amazon’s custom Trainium and Graviton chips. The move deepens integration between Anthropic and AWS, allowing Claude to be accessed natively within AWS services. Over 100,000 organizations already use Claude via Amazon Bedrock. This investment is part of a broader AI infrastructure race, with Amazon planning around $200 billion in capital expenditures this year, largely focused on expanding AI compute capacity.

marsbit04/21 04:09

Amazon Invests Additional $25 Billion in Anthropic, AI Infrastructure 'Arms Race' Escalates

marsbit04/21 04:09

The DeepSeek You've Been Waiting For Has Long Changed

The article discusses the delayed release of DeepSeek V4, a highly anticipated AI model in China, and explores the reasons behind its slowed development. Initially a leader in the global AI race, DeepSeek has fallen behind competitors like OpenAI, Anthropic, and Google, which release major updates every few months. A key factor is DeepSeek's shift in focus due to national strategic priorities. In early 2025, the Chinese government encouraged the company to use Huawei’s Ascend processors instead of NVIDIA’s GPUs, aligning with broader efforts to achieve technological self-reliance. DeepSeek attempted to train its models on Huawei’s Ascend 910C chips but faced technical challenges, including instability and communication issues during distributed training. As a result, the company continued using NVIDIA hardware for training while only using Ascend chips for inference. In 2026, DeepSeek prioritized adapting V4 to Huawei’s new Ascend 950PR and Cambricon chips, aiming for a full migration from NVIDIA’s CUDA to Huawei’s CANN framework. This adaptation process, particularly ensuring precision alignment across hardware, consumed significant time and resources, slowing down model iteration. The delay also reflects DeepSeek’s evolving role from a purely market-driven entity to a "national mission-oriented" company. This shift has come at a cost: the model now lags behind competitors in areas like code generation and multimodal capabilities, and the company has faced talent drain, with key researchers leaving for better-paying opportunities at larger tech firms. Despite these challenges, V4’s release is seen as a potential milestone for China’s AI industry, demonstrating that advanced models can run on domestic hardware ecosystems. While it may not be a groundbreaking model in terms of performance, its success could validate China’s broader strategy for AI independence.

marsbit04/15 10:32

The DeepSeek You've Been Waiting For Has Long Changed