# 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.

Microsoft is Afraid of Being Marginalized by AI Giants

Microsoft, once the defining force of the PC era, now faces a familiar challenge in the AI age: the risk of being relegated to a profitable but invisible infrastructure provider. This anxiety was laid bare at Build 2026, where CEO Satya Nadella unveiled a major strategic pivot. The catalyst was a quiet April agreement that dissolved Microsoft's exclusive licensing and cloud-hosting deal with OpenAI, its once-vital partner. This erased Microsoft's key AI moat. With OpenAI and Anthropic defining AI applications and gaining enterprise traction—even within Microsoft's own ranks—Nadella had to answer: without exclusivity, what is Microsoft's role? The answer was a suite of seven in-house AI models, a developer-focused AI workstation (Surface RTX Spark Dev Box), and, most crucially, the Agent 365 platform for enterprise AI governance. The models, notably targeting Anthropic's strengths in coding and enterprise, signal a defensive move. However, the broader strategy is to make the models themselves less decisive. Financially, Microsoft's AI revenue is strong, driven largely by Azure running others' models. Yet its user-facing products like Copilot show weak penetration and engagement. Microsoft earns infrastructure money but lacks direct user mindshare. Nadella's core fear is being "hollowed out." As OpenAI and Anthropic prepare for IPOs and gain financial independence, they may build their own infrastructure, threatening Azure's lucrative AI revenue stream. Microsoft's window is to entrench itself deeper: not as the model creator, but as the indispensable platform for securely deploying, managing, and governing all AI models within the enterprise through Agent 365. Build 2026 revealed Microsoft's bet: in the AI era, the ultimate power lies not in any single model, but in the enterprise "operating system" that controls them. Nadella is determined to ensure Microsoft is the driver of this new era, not just a passenger.

marsbit06/03 11:03

Microsoft is Afraid of Being Marginalized by AI Giants

marsbit06/03 11:03

CPU, Quietly Returning to the Center of the AI Computing Power Stage

Over the past three years, AI computing power narratives have been dominated by GPUs. However, starting in 2026, this story began to shift. While training large models remains GPU-intensive, the rapid growth of inference and AI agent workloads, which require high levels of task orchestration, concurrency, and data flow management, has highlighted a renewed critical role for CPUs. These are tasks GPUs are not designed to handle. Intel's recent launch of the Xeon 6+ processor, built on its Intel 18A process and featuring up to 288 efficiency cores (E-cores), exemplifies this strategic pivot. It is positioned not as a mere companion to GPUs but as the essential "control plane" for AI infrastructure, optimized for high-density, energy-efficient, and high-throughput workloads characteristic of AI agents and inference. This "CPU resurgence" is not about CPUs outperforming GPUs in raw computation. It reflects a systemic bottleneck: as AI scales from training single models to deploying countless intelligent agents, the demand for coordination and data handling surges. Major cloud providers are also developing their own high-density ARM-based server CPUs for similar workloads. However, Intel's success with this strategy faces significant challenges. Competition includes NVIDIA's integrated CPU-GPU solutions, the expanding adoption of cloud vendors' in-house ARM CPUs, and the crucial market test of Intel's 18A manufacturing process against rivals like TSMC's N2. In conclusion, CPUs are indeed reclaiming a central, though redefined, role in AI compute—managing the complex orchestration that enables massive-scale AI deployment. While the trend is clear, which company will ultimately lead this CPU resurgence remains an open question to be decided in the data centers of 2027 and beyond.

marsbit06/03 10:42

CPU, Quietly Returning to the Center of the AI Computing Power Stage

marsbit06/03 10:42

Morningstar Valuates SpaceX at Only $780B, Less Than Half of IPO Target, 'Largest IPO Ever' Overpriced?

Morningstar values SpaceX at $780 billion ahead of its IPO, far below the company's $1.75 trillion target. The research firm's DCF model values the core launch and Starlink businesses at $611 billion and assigns only a $170 billion probability-weighted valuation to the AI segment (including xAI). Analyst Nicolas Owens calls the company "severely overvalued," citing a near-100x price-to-sales ratio based on 2025 revenue. Despite the bearish long-term view, Morningstar acknowledges short-term price support from low float and potential rapid inclusion in the Nasdaq 100. The IPO is set for June 12, featuring an unusual tiered lock-up expiration that could create selling pressure over time.

marsbit06/03 05:01

Morningstar Valuates SpaceX at Only $780B, Less Than Half of IPO Target, 'Largest IPO Ever' Overpriced?

marsbit06/03 05:01

Optical Modules Soar, Why Is NOK the Second Leader After MRVL?

Nokia's stock has surged nearly 170% to around $16.8 since Nvidia's $1 billion investment and AI-RAN partnership in October 2025, reflecting a market re-rating from a cyclical telecom equipment provider to an AI infrastructure player. This rise, adding roughly $60 billion in market cap, is driven by AI capex expansion into telecom edge, RAN, and optical networks. The company's Q1 2026 results showed strong momentum, with AI & Cloud net sales up 49% and 10 billion euros in new orders, prompting Nokia to raise its AI & Cloud market growth forecast to a 27% CAGR (2025-2028). Optical network growth of 20% further strengthens its position in connecting AI data centers. Recent tests with operators like T-Mobile and the opening of an AI Networking Innovation Lab demonstrate progress from concept to early commercial deployment. Nokia's strategy integrates Nvidia GPUs into its network hardware, enabling concurrent AI processing and RAN tasks for real-time optimization and new edge services. However, with a trailing P/E nearing 100x and consensus price targets lagging the current stock price, significant future growth is already priced in. The key constraint now is the pace and scale of large-scale operator deployments. While execution signals remain positive and the company's position in AI edge infrastructure is established, high valuation leaves limited room for error, making tangible commercial contracts the critical factor for further stock performance.

marsbit06/03 04:39

Optical Modules Soar, Why Is NOK the Second Leader After MRVL?

marsbit06/03 04:39

For Hedging, Buy Gold and Oil; For Explosive Growth, Buy AI; Bitcoin, the 'Outdated' Asset, Enters a Bear Market

Bitcoin’s price has recently fallen sharply, hitting a two-month low near $66,000, with Ethereum also dropping to a three-month low. While surface explanations point to ETF outflows, geopolitical tensions, and corporate selling, a deeper issue is emerging: Bitcoin is losing a crucial asset competition. For years, Bitcoin thrived in a low-rate environment where investors sought alternatives amid inflation fears and dissatisfaction with traditional options. Now, the market landscape has shifted, leaving Bitcoin stuck in an "awkward middle ground," facing challenges on three fronts: 1. **As an inflation hedge, gold is winning.** Investors worried about persistent inflation are turning to tangible assets like gold, energy stocks, and commodity producers, which offer more direct pricing power and physical backing. 2. **For growth exposure, AI is winning.** Those seeking high growth now favor AI-related companies with actual revenues and profits, an area where Bitcoin's lack of cash flow puts it at a disadvantage. 3. **Within crypto, infrastructure and stablecoins are winning.** Even investors wanting crypto exposure have alternatives like exchanges, stablecoin issuers, and tokenization firms, whose performance is directly tied to real-world adoption and offers clearer operational leverage. The recent market reaction to inflation warnings highlights this shift. Instead of boosting Bitcoin as "digital gold," such news now drives flows toward traditional inflation-sensitive assets. Therefore, recent events like ETF outflows and corporate selling are seen not as causes, but as symptoms of this new reality. Capital has more compelling options, and investors are becoming more selective. The emerging bear case for Bitcoin is no longer about it being a fraud or failed technology, but rather that **scarcity alone is no longer enough**. It is no longer seen as the best hedge, the best growth asset, or the only crypto play.

marsbit06/03 02:19

For Hedging, Buy Gold and Oil; For Explosive Growth, Buy AI; Bitcoin, the 'Outdated' Asset, Enters a Bear Market

marsbit06/03 02:19

SaaS Battle Royale: The Survivors Who Win All Share One Common Trait

**Summary** The AI revolution has triggered a "SaaS apocalypse," forcing a brutal market shakeout. The key dividing line is the pricing model. Companies like Snowflake and Datadog, which charge based on consumption (e.g., data processed or compute used), are thriving. AI workloads actively *generate* more demand for their services, fueling growth. Datadog's accelerating revenue is a prime example. Microsoft and Palantir, as platform/ecosystem players, also benefit by acting as essential channels for AI deployment. In contrast, traditional SaaS firms built on per-seat or per-task licensing (e.g., Intuit, Adobe) face direct pressure, as AI threatens to automate the very human tasks their software supports. Companies like Salesforce, a per-seat giant, are caught in the middle. While showing strong AI monetization (e.g., its Agentforce platform) and experimenting with consumption-based "Flex Credits," its stock remains under pressure, illustrating that the market rewards *completed* transitions, not just the intent. The recent Microsoft Build conference underscored key trends: AI is evolving from an assistant to an autonomous "agent," and platform providers like Microsoft are consolidating their control. The market's recovery is highly selective, focused on identifying which companies are "fed by AI" versus "eaten by AI." Future focus will be on the diffusion of this recovery to transforming companies and the real-world adoption data of AI agents like Microsoft Copilot.

marsbit06/03 02:02

SaaS Battle Royale: The Survivors Who Win All Share One Common Trait

marsbit06/03 02:02

Can DeepSeek Save China One Trillion Dollars?

"DeepSeek and the $1 Trillion Infrastructure Question" The article examines whether DeepSeek's AI optimization breakthroughs could potentially save China $1 trillion in future AI infrastructure costs. The analysis begins with Nvidia's upcoming Vera Rubin AI platform, costing ~$7.8 million, where memory (HBM4/LPDDR5X) constitutes $2 million—a 435% cost increase in one year, highlighting how AI hardware spending is shifting toward expensive memory components. DeepSeek's approach works in the opposite direction. Through three key technical innovations showcased in DeepSeek V4, the company dramatically improves hardware efficiency: 1. **Memory Compression (MLA)**: Re-engineers the attention mechanism to compress long-context memory (KV Cache) by over 90%, drastically reducing expensive HBM usage. 2. **Selective Activation (MoE)**: Employs Mixture-of-Experts architecture where only a small fraction of parameters (e.g., 49B out of 1.6T in V4-Pro) are activated per token, allowing most parameters to reside in cheaper memory/SSD. 3. **Computation Caching**: Reuses previously computed results via cache hits, replacing expensive GPU computations with cheap memory reads. Combined, these optimizations allow the same hardware to produce approximately 4x more tokens, effectively reducing required hardware investment by 75%. DeepSeek's pricing reflects this: a 10-billion token workload costs ~$522 monthly versus ~$9,000-$10,000 for competitors. The $1 trillion savings projection stems from McKinsey's estimate that global AI infrastructure will require ~$5.2 trillion investment by 2030. As China's daily token consumption grows toward quadrillions, even marginal efficiency gains scale massively. With a conservative 4x throughput improvement, China could avoid building tens of thousands of AI data centers equivalent to ~7 trillion RMB ($1 trillion) in saved investment. Critically, this strategy shifts dependency from scarce, expensive GPU/HBM—where China lags—toward more accessible storage, caching, and systems engineering where domestic suppliers like CXMT are gaining strength. Rather than "replacing Nvidia," DeepSeek rebalances AI's value chain away from monolithic hardware dependency. Ultimately, DeepSeek's technical breakthroughs could lower the barrier to AI adoption across Chinese industries by making advanced capabilities affordable at scale—transforming who can access next-generation AI.

marsbit06/03 00:47

Can DeepSeek Save China One Trillion Dollars?

marsbit06/03 00:47

Hedge by Buying Gold and Oil, Chase Soaring Returns with AI. ‘Dated’ Bitcoin Enters a Bear Market

Bitcoin has recently declined, hitting a two-month low near $66,123, while Ethereum fell to a three-month low around $1,837. Analysts suggest the drop is not merely due to factors like ETF outflows or MicroStrategy's selling but reflects a deeper issue: Bitcoin is losing a broader asset competition. In a near-zero interest rate environment, Bitcoin previously thrived as an outlet for investor dissatisfaction with inflation and limited options. However, the market landscape has shifted. Bitcoin now occupies an "awkward middle ground," facing competition on three fronts. For inflation hedging, investors prefer gold, energy stocks, and commodity producers—assets with tangible backing and clearer pricing power. For growth exposure, AI-related companies with actual revenues and profits are more attractive. Even within crypto, investors can choose stablecoins, exchanges, or infrastructure firms tied directly to adoption, offering clearer business models and leverage. Thus, Bitcoin is no longer the top choice for hedging, growth, or crypto exposure. This shift is evident in market reactions: despite recent warnings about persistent inflation from a Fed official, Bitcoin did not rally as it might have in the past. Instead, capital flowed to assets with direct commodity or energy exposure. The recent ETF outflows and MicroStrategy sales are symptoms, not causes, of this new reality. Investors are becoming more selective, demanding clearer value propositions beyond mere scarcity. The emerging bear case for Bitcoin is not about it being a bubble or failed technology, but that scarcity alone is no longer sufficient.

华尔街日报06/03 00:40

Hedge by Buying Gold and Oil, Chase Soaring Returns with AI. ‘Dated’ Bitcoin Enters a Bear Market

华尔街日报06/03 00:40

Missed the AI Stock Surge, What to Do Now?

The article addresses investor anxiety over having missed the explosive, multi-year rally in AI stocks (e.g., NVIDIA, Micron, SK Hynix) across global markets. It notes that despite the broad bull run, data shows most retail investors lost money in 2025, and even major institutions like Bridgewater and Berkshire Hathaway significantly reduced holdings or held excessive cash, thereby missing substantial gains. The piece highlights common pitfalls: panic selling during corrections, misdirected investments into underperforming AI verticals, and premature profit-taking. It argues that AI represents a foundational, long-term technological shift comparable to electricity or the internet, implying the opportunity is not over. Citing figures like Jack Ma and Ma Huateng, the article suggests the AI wave is just beginning, with vast potential across the entire产业链, from infrastructure to applications. The conclusion advises against chasing trends blindly. Instead, it recommends a disciplined, long-term approach—focusing on one's own strengths within the expansive AI ecosystem to prepare for the next structural opportunities.

marsbit06/03 00:19

Missed the AI Stock Surge, What to Do Now?

marsbit06/03 00:19

After 540,000 Lines of Code, Garry Tan Realizes the Old Game of AI Programming Is Over

YC President Garry Tan's project "Garry's List" involved over 540,000 lines of Rails code, but he concluded this approach is outdated. He argues the software industry is stuck in a "Foxconn factory" mindset: building excessive tests, validators, and control logic to constrain LLMs, which have become cheap and capable enough to work autonomously. The old paradigm treated LLM calls as expensive and code as cheap, leading to complex systems to "manage" the AI. This has now reversed. The future lies in "just-in-time software"—using natural language instructions (Markdown-based "skill packs") and minimal code, letting the AI handle the work. Tan advocates for "skillifying" workflows: after an agent completes a task, it packages the process into a reusable, tested skill pack. This shifts value from writing code to designing capabilities. For example, his agent reviewed 85 hackathon submissions in 30 minutes, a task that previously took days. He emphasizes "tokenmaxxing"—willingly spending on LLM tokens to gain a years-long competitive advantage, as costs are plummeting. The core shift is from measuring output in lines of code to focusing on clarity, taste, and judgment. The best future engineers won't write the most code, but will know what to build and how to unlock the most intelligence with the least code.

marsbit06/02 21:38