# Пов'язані статті щодо Storage

Центр новин HTX надає останні статті та поглиблений аналіз на тему "Storage", що охоплює ринкові тренди, оновлення проєктів, технологічні розробки та регуляторну політику в криптоіндустрії.

From 'Old Dogs' to 'New Darlings': How AI is Revaluing Old Infrastructure, from Dell to Nokia

"Old Dogs" Become AI's New Darlings: Revaluing Legacy Infrastructure The AI investment narrative is shifting. Beyond the spotlight on core chipmakers like Nvidia, a new wave of interest is rising for legacy tech companies—Dell, HPE, Nokia, Cisco, Corning, Western Digital—once labeled as slow-growth, outdated stories. This resurgence stems from AI's evolution from model development to real-world deployment, creating massive demand for physical infrastructure. As AI moves into data center construction and enterprise adoption, the focus turns to who can actually build and deliver complex systems. These established players hold decades of experience in supply chains, integration, networking, and enterprise delivery—assets now critical for scaling AI. The revaluation can be grouped into three key infrastructure areas: 1. **Servers & Integration (e.g., Dell, HPE):** They are becoming essential system integrators, transforming GPUs into full-scale AI servers with networking, power, and cooling, then delivering them to clients. Strong recent earnings and AI-specific revenue/order growth for Dell and HPE underscore this shift. 2. **Networking & Connectivity (e.g., Corning, Nokia, Cisco):** As AI clusters grow, high-speed data transfer becomes paramount. Corning benefits from fiber demand for data center links, Nokia is exploring AI-integrated wireless networks (AI-RAN), and Cisco sees surging orders for data center switches—all critical for efficient AI operations. 3. **Storage (e.g., Western Digital, Seagate):** The AI data explosion requires vast capacity. Beyond high-speed memory (HBM), there's growing need for high-capacity HDDs to store training data, logs, video, and cold/archival data cost-effectively. This revaluation, however, is not a blanket endorsement. True reassessment requires concrete proof: AI-driven orders and revenue growth, upward revisions to company guidance, and sustainable improvements in profit quality, not just top-line sales. In essence, AI is not turning all old tech firms into high-growth stocks; it is selectively re-pricing the "old assets" of companies that are mission-critical for building the new AI infrastructure, transforming their legacy capabilities into renewed growth engines.

marsbit9 год тому

From 'Old Dogs' to 'New Darlings': How AI is Revaluing Old Infrastructure, from Dell to Nokia

marsbit9 год тому

From 'Old Guys' to 'New Favorites': How AI Is Revaluing Old Infrastructure from Dell to Nokia?

From "Vintage Tech" to "New AI Darlings": How AI Revalues Old Infrastructure One year ago, tech giants like Dell, Nokia, Cisco, and Western Data were seen as slow-growth, low-valuation stories, far from the AI spotlight dominated by players like Nvidia. Now, these legacy tech stocks are gaining market attention, sparking debate on whether this is genuine industry revaluation or a temporary narrative. As AI moves from model parameters to real-world data centers, the market is recognizing companies with proven delivery and infrastructure capabilities. This shift marks a change in the AI investment thesis: from pure model and GPU focus to the complex systems engineering required for deployment. Companies like Dell, HPE, and Corning are being revalued not for being "sexy" AI innovators, but for their decades of accumulated expertise in supply chains, enterprise delivery, and infrastructure—assets that have become critical in the AI buildout phase. The revaluation is unfolding across three key infrastructure lines: 1. **Servers & System Integration:** Dell and HPE are emerging as crucial system integrators or "general contractors" for AI data centers, translating GPU orders into complete, deployable server racks integrated with power, cooling, and networking. 2. **Networking & Connectivity:** AI's scale demands robust high-speed connections. Corning (fiber optics), Nokia (AI-RAN, 6G), and Cisco (data center switches) are gaining importance for enabling efficient data transfer within and between AI clusters. 3. **Storage:** Beyond high-speed memory (HBM/DRAM), the AI data explosion is driving demand for high-capacity hard drives (HDDs) from companies like Western Digital and Seagate to handle training data, logs, and cold storage cost-effectively. For this revaluation to be substantive and not just a narrative, three criteria are key: 1) Concrete AI-related order and revenue growth (e.g., Dell's AI server sales), 2) Upward revisions to company financial guidance, and 3) Sustainable improvements in profit quality, not just top-line revenue spikes. In essence, AI's transition to a real construction phase is re-pricing "old assets" against "new demand." The opportunity, however, is selective. Only those legacy firms that are demonstrably integrated into the capital expenditure chains of data center and enterprise AI deployment are likely to experience a true "logic re-rating" rather than just a temporary valuation bounce.

marsbit22 год тому

From 'Old Guys' to 'New Favorites': How AI Is Revaluing Old Infrastructure from Dell to Nokia?

marsbit22 год тому

From Token Explosion to Physical Bottlenecks: The Storage Bull Market Driven by Agentic AI

**From Token Explosion to Physical Bottlenecks: The Agentic AI-Driven Storage Bull Market** The AI semiconductor narrative is shifting from training to inference, which now accounts for 66% of AI compute. In the inference "Decode" phase (autoregressive token generation), GPU performance is bottlenecked by memory bandwidth and capacity, not raw compute (FLOPS). The key constraints are **HBM (High Bandwidth Memory) bandwidth** (determining token generation speed) and **HBM capacity** (determining how many requests/models can be served simultaneously). This creates a core economics equation: Token cost is proportional to (GPU + power cost) divided by Tokens/sec, which is fundamentally limited by HBM specs. This drives unprecedented demand for advanced storage. **HBM**, a 3D-stacked DRAM, is critical for AI accelerators. Its complex production consumes 3-4x more wafer capacity than standard DRAM, squeezing supply for traditional memory (DDR) and causing severe shortages. **HBF (High Bandwidth Flash)**, an emerging high-bandwidth NAND, aims to bridge the gap between HBM speed and SSD capacity for AI model weights. The market is experiencing a historic, structurally driven super-cycle. Demand is fueled by a triple engine: 1) AI training (parameter arms race), 2) AI inference explosion (especially Agentic AI with long contexts), and 3) general data center expansion. Supply is constrained by the HBM产能挤压 effect and the 2-3 year lead time for new fab capacity. Analysts project a DRAM supply deficit of ~5% in 2026. Inventory across the supply chain is at historically low levels, with OEMs securing long-term agreements (LTAs) locking in future supply. Current indicators (Q2 2026) suggest the cycle is in its mid-phase, not peaking. While spot prices have corrected from highs, contract prices are forecast to rise sharply (e.g., +70-75% QoQ for NAND). Capacity utilization remains high, and inventory days are still low. The cycle is expected to peak around mid-2027. The storage landscape is stratified, with key players in HBM (SK Hynix, Samsung, Micron), NAND/SSD/HBF (Samsung, Kioxia/WD, SanDisk), and NOR Flash (Winbond, GigaDevice) well-positioned for this AI-driven era.

marsbit05/22 03:41

From Token Explosion to Physical Bottlenecks: The Storage Bull Market Driven by Agentic AI

marsbit05/22 03:41

AI Saved a Group of New Energy Investors

The article "AI Saves a Group of New Energy Investors" details a remarkable turnaround in the green energy investment sector, driven by its convergence with artificial intelligence infrastructure. After a prolonged downturn marked by valuation slumps and funding cold spells since 2022, the sector has experienced a dramatic resurgence in 2026. This shift is attributed to new policies, particularly the "AI-Energy Synergy" national strategy, which mandates green power and energy storage systems for new large-scale computing centers. This redefines green electricity and storage from traditional manufacturing into core, indispensable assets for AI's operational backbone, creating a new narrative where "computing power equals electricity, and green power equals assets." This paradigm change is reflected in surging market performance. Power stocks like Datang Power have seen massive gains, and green energy ETFs have recorded significant capital inflows. The IPO market is also active, with companies like Sige New Energy listing successfully. Investment and financing have accelerated sharply, with major expansion projects and large-scale IPOs like China Resources New Energy's record-breaking offering. Notably, some top projects have seen valuations rebound by approximately 60%. The article highlights that the previous industry trough became a prime investment window. With AI-driven demand predicted to create massive power shortfalls (e.g., a projected 55GW gap for data centers), sectors like energy storage, grid upgrades, and green power are seeing explosive growth. Investors are now prioritizing areas like power management, large-scale storage, virtual power plants, and supporting technologies like liquid cooling—the "pick-and-shovel" plays of the AI infrastructure boom. Examples like KKR's highly successful investment in cooling company CoolIT Systems underscore the lucrative opportunities. In conclusion, the integration with AI has sparked a fundamental revaluation of new energy assets. For investors who endured the sector's低谷, a harvest season has arrived, with the broader investment upswing seemingly just beginning.

marsbit05/20 11:53

AI Saved a Group of New Energy Investors

marsbit05/20 11:53

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