# Artikel Terkait Data Center

Pusat Berita HTX menyediakan artikel terbaru dan analisis mendalam mengenai "Data Center", mencakup tren pasar, pembaruan proyek, perkembangan teknologi, dan kebijakan regulasi di industri kripto.

ARM's Stock Price Soars 30% Against the Trend, Is ARM, Now Making AI Chips, Winning Big?

ARM's stock surged over 15% on May 21, 2026, reaching a record high of $259, driven by its strategic pivot beyond its traditional IP licensing business. For over three decades, ARM has profited by licensing chip designs to companies like Apple and Qualcomm, earning mere cents per chip. However, with the mobile market maturing, growth stalled. In March 2026, ARM announced a historic shift: it would design and sell its own finished chips for the first time. Its "AGI CPU," built for AI data centers, targets the growing computational needs of AI Agents—tasks like workflow orchestration and data preprocessing where CPUs are crucial. This move positions ARM directly in the high-value server CPU market, competing with some of its own licensees. Analysts believe the rise of Agentic AI will dramatically increase demand for data center CPUs. Bernstein set a $300 price target, forecasting ARM's annual revenue could reach $26 billion by 2030 as the server CPU market expands. Major customers like Meta and OpenAI have already signed on for the AGI CPU, with committed demand reportedly doubling to over $2 billion within six weeks of launch. While this transformation offers massive upside, risks remain. ARM's valuation is extremely high (P/E ~300), pricing in future success. The company must also navigate potential conflicts with existing partners and execute flawless chip manufacturing. Nevertheless, Wall Street is betting that ARM's move from a "tax collector" to an AI infrastructure provider could redefine its growth trajectory for the AI era.

marsbit17j yang lalu

ARM's Stock Price Soars 30% Against the Trend, Is ARM, Now Making AI Chips, Winning Big?

marsbit17j yang lalu

AI Server Power Supply Undergoes Major Transformation, ADI Bets Big with a $1.5 Billion Investment

**Title: AI Server Power Supply Undergoes Major Shift as ADI Makes $1.5 Billion Bet** **Summary:** Analog Devices Inc. (ADI) has announced a definitive agreement to acquire Empower Semiconductor in an all-cash transaction valued at approximately $1.5 billion. This move highlights the critical and growing importance of advanced power delivery technologies in the era of data-intensive AI computing. The acquisition targets Empower's key technologies that address fundamental power challenges in high-performance AI data centers: **Integrated Voltage Regulators (IVR)**, which integrate dozens of discrete components into a single IC for high density and nanosecond transient response; **ECAP Silicon Capacitors (SiCaps)**, offering ultra-low ESL/ESR for high-frequency filtering; **Vertical Power Delivery (VPD)** architecture, which reduces transmission distance and losses; and the overarching **FinFast** technology platform. ADI's strategy aims to fill the "last millimeter" gap in power delivery from the board level to directly beneath the processor die. The deal follows ADI's recent product launches and strategy focused on AI data center power, including µModule solutions, SiC switches, and 800V high-voltage DC systems. The article details the industry-wide trend towards higher integration and VPD to manage soaring GPU/accelerator power demands, now reaching kilowatt levels per card. It examines the three evolutionary stages of AI power: traditional lateral power delivery, VPD, and ultimately substrate-integrated voltage regulators (SIVR). Competitors like Infineon, MPS, Vicor, and TDK are also advancing VPD solutions, while companies like Murata, Samsung Electro-Mechanics, and Rohm are leading in silicon capacitor development. In conclusion, as AI server power consumption escalates dramatically, technologies like IVR, SiCaps, and VPD are becoming essential for efficient power delivery within constrained spaces. ADI's significant investment signals an urgent industry need for innovation in this domain.

marsbit20j yang lalu

AI Server Power Supply Undergoes Major Transformation, ADI Bets Big with a $1.5 Billion Investment

marsbit20j yang lalu

$67 Billion! AI Boom Fuels Largest Energy Merger in U.S. History

In a landmark deal, NextEra Energy announced a $67 billion acquisition of Dominion Energy on May 18, 2026, marking the largest U.S. utility merger. The primary driver is the insatiable power demand from AI data centers, concentrated in Virginia's "Data Center Alley," where Dominion is the key supplier holding over 51 GW of data center contracts. This acquisition highlights a fundamental shift. While global electricity demand grew 3% in 2025, data center demand surged 17%, driven by AI. This strain is causing irreversible price spikes, like the 76% increase in the PJM grid, and grid instability, as seen in a 2025 Virginia incident where 60 data centers disconnected simultaneously. NextEra, a major renewable energy provider, bets that combining its clean power and storage expertise with Dominion's strategic access to data centers will be transformative. However, the deal raises critical questions about cost distribution. Analysts warn that up to $700 billion in infrastructure costs may be passed to residential consumers through higher bills, creating a disparity where private AI profits rely on publicly subsidized grid upgrades. This merger signals just the beginning of AI's profound reshaping of the energy landscape.

marsbitKemarin 08:07

$67 Billion! AI Boom Fuels Largest Energy Merger in U.S. History

marsbitKemarin 08:07

NVIDIA's Q1 Performance is Solid, Vera CPU Drives Future Increment

NVIDIA reported solid Q1 FY2027 results and Q2 guidance, largely meeting optimistic investor expectations. Revenue reached $81.62 billion, up 85% year-over-year, with adjusted EPS of $1.87 beating estimates. The company re-segmented its business, highlighting Data Center as the core growth driver, with Hyperscale revenue surging 115%. The most significant new information was the unveiling of the Vera CPU, targeting a new $200 billion market for Agentic AI. It can be sold alongside Rubin GPUs or independently, with production starting in Q3. Management reaffirmed the $1 trillion revenue target for the Blackwell and Rubin platforms through 2027. Q2 revenue guidance of approximately $91 billion aligns with expectations. However, the announcement of a new $80 billion share buyback authorization and a raised dividend, while positive, fell slightly short of some investors' hopes for a larger repurchase plan.

marsbitKemarin 02:20

NVIDIA's Q1 Performance is Solid, Vera CPU Drives Future Increment

marsbitKemarin 02:20

Decoding the Latest Holdings of "The Son of Wall Street Version" Leopold Aschenbrenner: Why Did the AI Bull King Turn to Short Nvidia?

Leopold Aschenbrenner, a renowned AI investor and former OpenAI researcher, has filed a 13F revealing a dramatic portfolio shift. He has taken a massive $8 billion short position against the entire semiconductor supply chain, including companies like NVIDIA, AMD, Broadcom, and ASML. This marks the first time in his fund's history that short exposure exceeds long exposure. His core thesis is that the bottleneck for AI investment is shifting from chip *design* to *infrastructure*, specifically power and memory. Accordingly, his long positions heavily target these areas. He maintains holdings in data center/Neocloud firms like CoreWeave, holds positions in power solutions like Bloom Energy, and has initiated new stakes in bitcoin mining companies such as CleanSpark and Riot Platforms due to their pre-existing grid access and power capacity. Aschenbrenner also increased his long bets on memory, exemplified by SanDisk, as AI models drive demand for NAND flash storage. Despite the aggressive short on semis, analysts suggest he may not be structurally bearish but rather views the chip trade as over-crowded, while seeing higher returns in infrastructure plays. Key risks identified include underestimating NVIDIA's entrenched CUDA ecosystem moat and the timing of his shorts.

marsbit2 hari yang lalu 09:50

Decoding the Latest Holdings of "The Son of Wall Street Version" Leopold Aschenbrenner: Why Did the AI Bull King Turn to Short Nvidia?

marsbit2 hari yang lalu 09:50

Sinking Servers into the Sea? They're Dead Serious About This

Sinking Servers into the Sea: A Serious Undertaking The article details China's launch of the world's first offshore, directly wind-powered, subsea data center in the East China Sea near Shanghai. This 1.95 billion yuan project houses over 2,000 servers in a submerged 10-meter-deep module. It is directly powered by a nearby offshore wind farm (over 95% green energy) and cooled by seawater. This innovative approach tackles the two core challenges of data centers: massive power consumption and heat dissipation. It achieves an exceptional Power Usage Effectiveness (PUE) of 1.15, far better than China's national average of 1.48, saving an estimated 61 million kWh of electricity annually. It also uses no freshwater and requires significantly less land. The concept builds upon earlier experiments, like Microsoft's Project Natick, which proved servers could reliably operate underwater with lower failure rates due to a stable, inert environment. The Shanghai project advances the model by co-locating with wind farms, simultaneously solving both the power source and cooling source problems in an economically viable way. This integration reduces infrastructure costs and eliminates grid transmission losses for the electricity used on-site. Looking ahead, the vision is to integrate data center modules directly into the foundations of future large-scale, deep-sea wind turbines. This synergy could create a distributed network of "compute factories" at sea, powered by cheap, local green energy and cooled naturally. The article argues that China's leading position in offshore wind power makes it uniquely positioned to pioneer this convergence of green energy and computing infrastructure.

marsbit2 hari yang lalu 04:29

Sinking Servers into the Sea? They're Dead Serious About This

marsbit2 hari yang lalu 04:29

The AI-Era Power Arms Race: Energy Order Reshuffle Behind NextEra's Acquisition of Dominion

The AI arms race is shifting from a focus on chips and models to a fundamental battle over electricity. NextEra Energy's proposed $66.8 billion acquisition of Dominion Energy highlights this profound change, as AI's explosive growth is rewriting the growth logic for the power sector. The deal is less about traditional utility consolidation and more about securing a strategic gateway to Virginia’s "Data Center Alley," a critical hub where tech giants have massive, signed load requirements. The core challenge is a growing disconnect: data center construction cycles are far shorter than the years needed to build new power generation and transmission infrastructure. Morgan Stanley predicts a 49GW gap in power availability for U.S. data centers by 2028. Electricity, once a taken-for-granted commodity, is now a scarce and strategic resource. This transforms the competitive landscape—future AI competitiveness may hinge not just on algorithms but on a company's ability to secure long-term, stable, and affordable power supply. The transaction signals a broader revaluation of the entire energy infrastructure chain, from natural gas and nuclear power for base load to storage and transmission equipment. However, the largest variable is regulation. Balancing rapid AI-driven grid expansion with public concerns over costs, fairness, and environmental impact will be a complex political and social challenge. The true value in the coming AI era may lie not just in power generation assets, but in owning the crucial infrastructure nodes, grid access rights, and the regulatory relationships needed to deliver electricity where it's needed most.

marsbit05/19 11:37

The AI-Era Power Arms Race: Energy Order Reshuffle Behind NextEra's Acquisition of Dominion

marsbit05/19 11:37

Bernstein's 97-Page Report Decoded: The Battle for AI Data Center Connectivity, Who Will Be the True Winner by 2026?

Bernstein's 97-page report analyzes the AI data center connectivity landscape. It argues that the bottleneck is shifting from raw compute (GPU) to the systems connecting GPUs, crucial for cluster efficiency. Copper and optical interconnects are not in a simple replacement cycle but will coexist long-term, with copper dominating short-distance "scale-up" connections and optics favored for longer "scale-out" scenarios. While Co-Packaged Optics (CPO) is the long-term direction for power and cost savings, its widespread adoption faces manufacturing and reliability hurdles, with mass deployment unlikely before 2028. Transitional technologies like Linear Pluggable Optics (LPO) and Near-Packaged Optics (NPO) are seen as near-term leaders. A key insight is that CPO will fundamentally reshape the value chain, shifting profits from traditional optical module suppliers towards chip designers (e.g., NVIDIA, Broadcom), advanced packaging (e.g., TSMC), and system integrators. For 2026, the report highlights more immediate and certain investment opportunities in the essential "infrastructure" enabling this connectivity shift. This includes upgrades for PCBs, ABF substrates, and CCLa driven by new AI server/switch platforms, alongside demand for 1.6T optical modules, LPO/NPO, and the testing/validation equipment required for future CPO scale-up.

marsbit05/19 03:16

Bernstein's 97-Page Report Decoded: The Battle for AI Data Center Connectivity, Who Will Be the True Winner by 2026?

marsbit05/19 03:16

After Storage, Are Copper and Fiber Optic Cables Facing an AI "Great Famine"?

Following the storage sector, copper and fiber optics are emerging as potentially the next major markets to experience explosive growth due to AI. Demand for copper, described by Goldman Sachs as "the oil of the AI era," is surging. Prices are near record highs, with LME copper up 41% over the past 12 months. This is driven by AI's immense and unique requirements: copper is the essential material for the massive electrical distribution (e.g., a 1GW AI data center requires ~27,000 tons) and advanced liquid cooling systems needed for high-power AI clusters like NVIDIA's GB200. Meanwhile, new large-scale copper mine discoveries have been scarce for a decade, tightening supply. Concurrently, a "fiber famine" is unfolding. AI's need for ultra-high-speed, long-distance interconnects between thousands of GPUs is pushing data transmission beyond the physical limits of copper cables. Demand for fiber optics is experiencing a step-change, with a single AI data center requiring up to 36 times more fiber than a traditional CPU rack. This has caused prices for standard G.652D fiber in China to nearly double in just three months. Supply is critically constrained due to the long (18-24 month) lead times required to expand production of the core preform material. In summary, AI's infrastructure demands are cascading down from semiconductors to foundational materials. Copper faces a structural supply-demand imbalance, while fiber optics is entering a period of severe shortage, positioning both as critical and potentially strained components of the AI build-out.

marsbit05/14 09:25

After Storage, Are Copper and Fiber Optic Cables Facing an AI "Great Famine"?

marsbit05/14 09:25

The Real AI Bubble, You Can't Buy It

The article argues that the real "bubble" in the current AI boom is largely invisible and inaccessible to the average investor. Unlike the 2000 dot-com bubble, where overvalued companies were publicly traded, the most significant value surges and financial risks are occurring in private markets. Core AI companies like OpenAI, Anthropic, xAI, and Databricks have seen valuations skyrocket (e.g., OpenAI's from $157B to $852B in 18 months), but these transactions happen through private secondary sales, not public stock exchanges. These opaque markets create an "anxiety exposure," leading public investors to chase indirect proxies like memory chip or utility stocks. The author highlights how AI wealth extraction has been radically front-loaded. Employees and founders can cash out years before a potential IPO through structured secondary sales, "founder-led secondary" deals, and collateralized loans against private equity. Major tech firms also use "acqui-hires" or technology licensing deals (like Google/Character.AI, Microsoft/Inflection AI) to secure talent and tech without full acquisitions, allowing early exits outside of regulatory scrutiny. Furthermore, the AI infrastructure build-out is compared to the 2008 real estate bubble. Massive data center projects are financed through complex, off-balance-sheet structures involving private credit, joint ventures, and asset-backed securities using GPUs as collateral (e.g., CoreWeave's deals). This creates a "shadow borrowing" system where the stability of future AI demand underpins trillions in debt, posing systemic risks if expectations falter. The recent collapse of SaaS company Pluralsight, financed by major private credit firms, is cited as a warning. The conclusion is that the most dangerous part of the AI bubble isn't in plain sight on public markets; by the time the average investor sees it, the critical wealth transfers have already occurred in private, unregulated spaces.

marsbit05/14 07:10

The Real AI Bubble, You Can't Buy It

marsbit05/14 07:10

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