# 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.

Which Companies Has NVIDIA's "Three-Track Investment Architecture" Invested In?

NVIDIA's investment strategy operates through a "three-track architecture," not just its NVentures venture arm. Corporate Development handles massive strategic bets (e.g., $30B in OpenAI, $10B in Anthropic, $20B in Synopsys). NVentures, a small team, focuses on early-stage, financial investments across sectors like quantum computing (Alice & Bob, PsiQuantum), AI infrastructure (OpenRouter, Tensormesh), and biotech. The NVIDIA Inception accelerator provides non-monetary support. This system allows NVIDIA to nurture startups and lock in strategic partners, creating a vast AI ecosystem. This aggressive capital deployment has drawn scrutiny. Critics like Michael Burry and EU regulators question potential "circular financing," where NVIDIA's equity investments in companies (e.g., CoreWeave, OpenAI) facilitate those companies' purchases of NVIDIA hardware, potentially inflating revenue. Supporters view it as a necessary "virtuous cycle" to secure supply and demand in a compute-scarce market. While NVentures' smaller deals appear like traditional VC, its role within the larger, controversial investment framework remains a point of debate.

marsbit06/04 06:42

Which Companies Has NVIDIA's "Three-Track Investment Architecture" Invested In?

marsbit06/04 06:42

AI Relay Stations Spark Heated Debate on Zhihu: Behind Cheap Tokens, What Are Users Really Worried About?

A discussion on Zhihu about "AI relay stations" shifted the niche developer topic of "cheap tokens" into broader user awareness. Users moved beyond simply questioning the legitimacy of these services to focus on practical concerns: Where do cheap tokens truly come from? Is the model being accessed the real one? Can relay stations see prompts, code, and API keys? For occasional users, are the risks worth it? The core debate centered less on price and more on trust. A primary worry is model authenticity—the risk of "model swapping," where users paying for a premium model might be routed to a cheaper one, creating an information asymmetry. Others argued that cost comparisons matter; while cheaper than official pay-as-you-go APIs, relay stations may not be the lowest-cost option versus subscriptions, domestic models, or free tiers, making user needs assessment crucial. Speculation about token sources ranged from legitimate bulk discounts to gray-area methods like account sharing or exploiting regional pricing. This opacity makes risk assessment difficult for users. Data security emerged as a critical concern, especially for enterprise use. When processing sensitive information like code, contracts, or client data, the inability to verify a relay station's data handling, retention, or access policies poses significant compliance and confidentiality risks. The evolving consensus suggests relay stations can be used cautiously for low-sensitivity, disposable tasks (e.g., summarizing public info, simple translation). However, they should not be the default for sensitive, professional, or production workflows involving proprietary data, Agents, or automated systems. Recommendations include avoiding large prepayments, not relying on a single service, using test prompts to monitor quality, anonymizing data where possible, and keeping official channels as backups. Ultimately, the discussion framed tokens not just as a billing unit but as a measure of real cost encompassing price, model integrity, data security, and service stability. The popularity of relay stations highlights user demand for affordable access, but the debate underscores a key trade-off: the savings from cheap tokens may come at the price of trust, transparency, and control over one's data and AI experience.

marsbit06/04 06:11

AI Relay Stations Spark Heated Debate on Zhihu: Behind Cheap Tokens, What Are Users Really Worried About?

marsbit06/04 06:11

ByteDance Adopts Arm CPUs, Jensen Huang: So Sad I Didn't Buy Arm

**Summary:** At Computex 2026, Arm CEO Rene Haas announced that ByteDance and Oracle have adopted Arm's self-designed Arm AGI data center CPU. The company expects significant revenue growth from this product, projecting $20 billion in demand for the 2027/2028 fiscal years. Haas noted that restricting AI-capable CPUs from the US to China is nearly impossible due to their widespread applications. Arm's stock has surged dramatically this year, notably rising 16% after NVIDIA's Arm-based Vera CPU and RTX Spark announcements. A highlight was the informal, humorous on-stage conversation between Haas and NVIDIA CEO Jensen Huang. Huang joked about NVIDIA's failed attempt to acquire Arm and playfully lamented selling his Arm shares. Both executives showed a clear sense of camaraderie and shared regret over the missed merger. Key technical topics were discussed: 1. **AI PC Design:** Huang explained NVIDIA's RTX Spark superchip (with a 20-core Arm CPU) is designed for future AI agents that will autonomously run and use tools on PCs, blending local and cloud processing. 2. **Agent vs. OS:** Huang emphasized the operating system remains crucial, as AI agents rely on its APIs and tools to function. 3. **Growth Constraints:** He identified the shift to "useful AI" that generates profitable tokens as a primary driver for immense, almost limitless, computational demand. Haas outlined Arm's strategy across PC and data centers. For PCs, Arm collaborates with partners like NVIDIA and MediaTek, offering its compute subsystem (CSS) for custom SoCs. In data centers, its Arm AGI CPU (built on TSMC's 3nm process) has gained major partners including OpenAI, Meta, and now ByteDance and Oracle. Arm presented a multi-year roadmap for its in-house CPU line. The article concludes that while GPUs dominated the AI training race, the explosion of AI agents is shifting significant focus to CPUs for inference, state management, and tool orchestration. The industry is trending towards vertical integration, with companies like cloud providers designing chips and chip/IP firms offering full solutions, all competing to deliver more efficient computing per watt.

marsbit06/04 04:51

ByteDance Adopts Arm CPUs, Jensen Huang: So Sad I Didn't Buy Arm

marsbit06/04 04:51

Broadcom's Q3 Guidance Misses Expectations by $12 Billion, After-Hours Trading Plummets Over 13%, AI Narrative "Cooling"?

On June 3, Broadcom released record Q2 FY26 results with revenue of $22.19B, up 48% YoY, and AI chip sales of $10.8B, up 143%. Adjusted EPS of $2.44 beat estimates. However, its Q3 AI semiconductor revenue guidance of $16B, while up over 200% YoY, fell roughly $1.2B (7%) short of analyst consensus expectations of $17.2B. This miss, coupled with slightly weaker-than-expected software revenue, triggered a severe market reaction. CEO Hock Tan maintained the FY26 AI revenue outlook of over $100B but did not raise it, disappointing investors who had priced in more robust growth. The stock plummeted over 13% in after-hours trading, erasing roughly $270B in market cap. The sell-off extended to peers like Marvell. A key concern for markets, particularly for Chinese optical module suppliers, was Tan's comment that the contribution of AI networking (e.g., Ethernet switches, optical interconnect chips) to AI revenue, currently near 40%, is expected to normalize to around 30% over time, signaling a potential peak in growth for that segment. Despite the guidance shortfall, Tan reiterated that AI demand remains "insatiable" and reaffirmed the long-term target of exceeding $100B in AI revenue by FY27. The reaction highlights the heightened sensitivity and premium valuation placed on AI-exposed stocks, where anything less than stellar guidance can prompt significant profit-taking. The broader question is whether this represents a cooling AI narrative or a correction in overstretched valuations.

marsbit06/04 04:47

Broadcom's Q3 Guidance Misses Expectations by $12 Billion, After-Hours Trading Plummets Over 13%, AI Narrative "Cooling"?

marsbit06/04 04:47

Claude Code Introduces Dynamic Workflows: Enabling AI to Form Teams and Collaborate

Claude Code introduces dynamic workflows, enabling AI to coordinate teams of specialized agents for complex tasks. This transforms Claude from a code assistant into a programmable workbench. Workflows address key limitations of single-agent systems: agentic laziness (premature task completion), self-preferential bias (favoring own outputs), and goal drift (losing sight of original objectives). The system allows Claude to dynamically create execution frameworks using JavaScript. It can split tasks, dispatch parallel agents for isolated work (e.g., in separate worktrees), implement adversarial validation, run tournaments, and synthesize results. This multi-agent approach is valuable for tasks requiring deep research, factual verification, code migration, root cause analysis, large-scale triage, and qualitative sorting. Key patterns include: classify-and-route, fan-out-and-synthesize, adversarial verification, generate-and-filter, tournaments, and loop-until-done. While token usage is higher, workflows excel where tasks resemble programming—needing problem decomposition, isolated context, hypothesis testing, and handling many details. They extend Claude Code's utility beyond technical work to areas like business plan review, resume screening, and naming brainstorm. The feature is not a universal solution but points to a future where AI tool competitiveness depends on organizing reliable, reusable, and auditable execution flows for complex goals.

marsbit06/04 02:15

Claude Code Introduces Dynamic Workflows: Enabling AI to Form Teams and Collaborate

marsbit06/04 02:15

A Nation Blocks Chips, a Giant Buys a Nuclear Power Plant: Why It's Time to Seriously Consider DeAI

**Title: Great Powers Blockade Chips, Giants Buy Nuclear Plants: Why It's Time to Seriously Consider DeAI** In May 2026, the US closed loopholes for Chinese firms to acquire advanced NVIDIA chips via overseas subsidiaries. That same month, Kenya halted a $1B geothermal data center project involving Microsoft, fearing its immense energy consumption. Meanwhile, Huawei announced mass production of its Ascend AI chip. These disparate events underscore a new reality: the competition for computing power ("compute") has escalated beyond the tech industry, becoming a geopolitical and infrastructural battleground. A new era of oligopoly is forming, with control over the AI stack—from GPU chips (NVIDIA) and cloud platforms (AWS, Azure, Google Cloud) to foundational models (OpenAI, Anthropic)—concentrating in a few Western "AI Octopus" corporations. This centralization creates systemic risks: pricing power and platform lock-in for users, infrastructure fragility, and a widening "compute divide" that threatens to marginalize nations without independent AI capacity. An "AI Iron Curtain" is deepening through export controls. In response, some nations like Saudi Arabia and the UAE are investing heavily to buy compute power, aiming to transition from oil to AI economies. The EU seeks to triple its compute capacity by 2030 to reduce dependency. However, the spending gap is vast, with four US tech giants alone planning ~$750B in AI capex for 2026. The race is increasingly constrained by energy, with AI tasks consuming up to 1000x more power than web searches, pushing firms to even acquire nuclear plants. This landscape is fueling interest in Decentralized AI (DeAI). It proposes a third way: using open protocols to coordinate a global network of idle GPUs, independent developers, and data centers, creating an AI infrastructure without a single controlling entity. Leveraging blockchain and cryptographic verification, DeAI aims to break market concentration, disperse energy demands, reduce geopolitical dependencies, and enhance transparency. While still nascent in performance and stability, DeAI's core promise is not immediate superiority but providing a crucial alternative architecture to resist monopoly, censorship, and centralized power. As specialized AI hardware costs fall and open-source models flourish, the window to build this foundation is open. The very existence of such competition serves as a vital check against the inevitable abuse of concentrated power.

marsbit06/04 00:53

A Nation Blocks Chips, a Giant Buys a Nuclear Power Plant: Why It's Time to Seriously Consider DeAI

marsbit06/04 00:53

Bitwise: Crypto Becomes a Contrarian Investment, Three Logics to Understand the Current Market

**Summary** Matt Hougan, Bitwise's CIO, analyzes the current crypto market through three key lenses, arguing it has shifted from a momentum-driven to a contrarian investment. **1) Crypto Becomes a Contrarian Play:** The market is weak, with major assets like Bitcoin and Ethereum down significantly. Capital has moved to hot sectors like AI, leaving crypto as an "unloved" asset class. This transforms crypto investing from trend-following to a test of patience and fundamental analysis. Investors now favor projects with solid fundamentals (e.g., Hyperliquid) over speculative ones. **2) Regulatory Overhang:** The uncertain fate of the U.S. CLARITY Act, a major crypto regulatory framework, is a key headwind. With its passage in 2024 seen as far from guaranteed (estimates range from 30-55%), institutional capital remains on the sidelines, choosing less risky alternatives like AI stocks. The market needs clarity—whether the bill passes or fails—more than any specific outcome to move decisively. **3) Capital Rotates to New Fundamentals:** This cycle differs from past bear markets where money fled to Bitcoin. Now, capital seeks smaller assets with strong use cases. While major cryptos fell in May 2024, tokens like Hyperliquid (+72%), Zcash (+50%), and XLM (+44%) rallied on their specific fundamentals. This rotation confirms the new contrarian, fundamentals-driven logic and signals the bear market may be in its later stages. **Conclusion:** Short-term pressure persists due to regulatory uncertainty and competition from AI narratives. Investing in crypto now requires a contrarian mindset—acting against the crowd and focusing on fundamental value. Patience and targeting high-quality projects based on their merits are essential for capturing long-term gains.

marsbit06/04 00:02

Bitwise: Crypto Becomes a Contrarian Investment, Three Logics to Understand the Current Market

marsbit06/04 00:02

WWDC26 Ultimate Preview: The All-New Siri is the Main Course, iOS 27 is Another Year of Refinements

Apple has confirmed WWDC26 will begin on June 8, with the keynote at 10 AM PT (1 AM Beijing Time, June 9). This year's focus is expected to shift significantly from routine OS updates to Apple's progress in AI, particularly a major overhaul of Siri. Reports indicate the highlight will be a new Siri, reportedly powered by Google's Gemini technology. This upgraded assistant is expected to appear as a lightweight bubble from the Dynamic Island and be accessible via a unified "Search or Ask" system-wide entry point. It aims to deeply integrate with iOS 27, iPadOS 27, and macOS 27, accessing personal data like messages, photos, and documents, with a potential standalone Siri app also in development. For iOS 27, leaks suggest incremental improvements rather than major redesigns. Key updates may include a redesigned, more customizable Camera app, enhanced photo editing tools within the Photos app, and potential early system optimizations for a future foldable iPhone. The update is also rumored to prioritize bug fixes, stability, and performance optimization. iPadOS 27 is anticipated to focus on improving productivity features like window management, file systems, and external display support to better utilize the iPad's hardware. macOS 27 is seen as a core platform for Apple Intelligence, likely receiving an optimized Siri, new AI features, and continued refinement of the "Liquid Glass" design language. Notably, macOS 27 may finally drop support for Intel-based Macs. The overarching theme for WWDC26 is whether Apple can effectively integrate AI across its ecosystem. The success of the new Siri and Apple Intelligence will be judged on their ability to move beyond standalone features and become a cohesive, context-aware system layer that understands user workflows across iPhone, iPad, Mac, and other devices, while maintaining Apple's emphasis on privacy and stability. The conference represents Apple's critical attempt to catch up and redefine the AI assistant experience after a perceived slow start in the generative AI era.

marsbit06/03 23:26

WWDC26 Ultimate Preview: The All-New Siri is the Main Course, iOS 27 is Another Year of Refinements

marsbit06/03 23:26

Crypto is dead, Perps are forever

The crypto industry is shifting from a focus on creating native assets (like altcoins and protocol tokens) to becoming a "global asset pipeline." Native cryptocurrencies, except for Bitcoin, are seen as failing in their value storage and utility promises, with demand driven largely by speculation. Attention and liquidity are now moving toward real-world assets (RWAs) like U.S. stocks, bonds, gold, and oil traded on-chain via perpetual contracts (Perps). Stablecoins like USDT and USDC set the precedent, proving blockchain's core strength is efficient global settlement and transfer, not inventing new monetary systems. Meanwhile, assets like Ethereum and many DeFi tokens struggle as their narratives weaken against tangible traditional assets and the rapid real-world progress of AI. Perpetual contracts have emerged as a pivotal innovation. They simplify trading by offering pure price exposure to any asset, bypassing complexities of ownership, custody, and traditional market hours. Projects like Hyperliquid gained traction by combining CEX-like efficiency with on-chain transparency, capitalizing on post-FTX distrust, macroeconomic volatility, and the surge in demand for 24/7 stock trading. In conclusion, while the era of speculative native "crypto assets" may be over, perpetual contracts persist as the industry's most potent financial instrument—transforming all assets into globally accessible, constantly tradable instruments centered on price speculation.

marsbit06/03 12:30

marsbit06/03 12:30

After Marvell's 32% Surge, the Chinese Chip Family Behind It Emerges

The stock price of Marvell Technology surged 32.5% on June 2nd, driven by NVIDIA CEO Jensen Huang highlighting its custom ASICs and optical interconnects as core to AI data center architecture. This event brought attention to the Chinese semiconductor family behind Marvell: the Dai siblings. The story centers on three siblings, all UC Berkeley graduates, whose three-decade entrepreneurial journey aligns with major semiconductor industry shifts. In 1995, youngest sister Dai Wei Li co-founded Marvell with her husband Sehat Sutardja and his brother, focusing on storage controllers. Eldest brother Dai Wei Min founded EDA company Ultima, later sold to Cadence, and later founded VeriSilicon (芯原) in China, becoming a leading semiconductor IP provider. Second brother Dai Wei Jin co-founded EDA firm Silicon Perspective (sold to Cadence) and GPU IP company Vivante, later acquired by VeriSilicon. The combined "Dai-Sutardja" family network extends beyond Marvell. Their ventures and investments form a comprehensive ecosystem for the post-Moore's Law, chiplet era. Key holdings include: Dream Big Semiconductor (AI SuperNICs, acquired by Arm), Alphawave (high-speed SerDes IP, acquired by Qualcomm), and Silicon Box (a chiplet advanced packaging foundry). VeriSilicon itself thrives on the AI ASIC and IP boom in China. Collectively, the family's AI infrastructure-related portfolio is estimated at over $22 billion. Their strategy represents a distinct path: building critical components for open standards and key manufacturing capacity in the chiplet era, rather than pursuing standalone AI chip dominance. While this path may not create the next NVIDIA, it has enabled repeated successful exits and sustained influence within the global semiconductor industry.

marsbit06/03 11:16

After Marvell's 32% Surge, the Chinese Chip Family Behind It Emerges