# Cloud Providers İlgili Makaleler

HTX Haber Merkezi, kripto endüstrisindeki piyasa trendleri, proje güncellemeleri, teknoloji gelişmeleri ve düzenleyici politikaları kapsayan "Cloud Providers" hakkında en son makaleleri ve derinlemesine analizleri sunmaktadır.

Tidal Investment: We Remain Bullish on the AI Industry Chain, But the Reasons Have Changed

Tidal Investment remains optimistic about the AI industry chain, but the rationale has shifted. The market narrative has changed. While recent large-scale IPOs (e.g., SpaceX) and major fundraising plans by tech giants like Alphabet and Meta have caused some nervousness, this isn't a sign of an AI peak. The focus has moved from the initial question of AI's viability to the sustainability of massive investment cycles. The key players—primarily the major cloud providers—are not slowing down; their capital expenditure (Capex) guidance for 2026 has been increased across the board (e.g., Alphabet to $180B, Amazon to $200B). This investment cycle is proving resilient and difficult to stop. Unlike traditional hardware cycles, current AI Capex is distributed across multiple physical layers—computing, memory, networking, and critically, power infrastructure. Bottlenecks are shifting from chips to elements like electricity, transformers, and cooling systems, which have much longer lead times and cannot be easily pre-built like fiber optics during the dot-com bubble. Supply chain data (e.g., Eaton's 240% YoY data center orders) confirms this broad-based, project-driven expansion. Market concerns are acknowledged but viewed differently. First, while Capex growth currently outpaces revenue growth, raising ROI questions, this mirrors the early scaling phase of cloud computing itself. A change in view would require concrete signals like downward Capex revisions or missed AI product targets, which haven't materialized by mid-2026. Second, comparisons to the 2000 dot-com bust are flawed. That crash was driven by a massive, parallel oversupply of cheap capacity (fiber). The current cycle faces *supply constraints* in critical, capital-intensive physical infrastructure that cannot be overbuilt as easily. In conclusion, the wave of fundraising reflects the next, more complex act of the AI story. Physical bottlenecks and sustained high Capex plans suggest this is not the finale but an ongoing, capital-intensive build-out phase. The script has changed, but the play is far from over.

marsbit06/25 10:36

Tidal Investment: We Remain Bullish on the AI Industry Chain, But the Reasons Have Changed

marsbit06/25 10:36

This Chip Sector Is on Fire

The global AI chip market is undergoing a significant paradigm shift, with ASICs (Application-Specific Integrated Circuits) emerging from a niche to a mainstream force, challenging the long-held dominance of GPUs in AI training. This "golden era" for ASICs is primarily driven by the industry's pivot from training to inference, where the cost and energy efficiency advantages of custom chips become critical for scaling to billions of users. Key signals include Google's TPU capturing 78% of its AI server shipments in Q1 2026, OpenAI's plans for a massive custom ASIC cluster with Broadcom, and cloud providers (CSPs) increasingly favoring in-house or custom designs for supply chain control and cost efficiency. Market forecasts are bullish: AI ASIC revenue is projected to hit $300 billion by 2027, with a 34% CAGR, potentially reaching a 45% share of the AI chip market. The competitive landscape is expanding beyond traditional leaders Broadcom and Marvell. MediaTek is aggressively targeting the data center ASIC market, projecting over $10 billion in 2026 revenue, while Qualcomm, leveraging its AlphaWave acquisition, is launching customized inference chips. These mobile chip giants are leveraging their SoC design expertise for a cloud-side transition. In China, companies like VeriSilicon and ASR Microelectronics are capitalizing on this trend as pivotal "enablers," providing full-stack ASIC design services and experiencing explosive order growth, particularly for cloud-side AI projects. However, challenges remain: high development costs, software ecosystem gaps compared to NVIDIA's CUDA, dependency on advanced packaging capacity (like TSMC's CoWoS), and the fundamental trade-off between customization and flexibility. The future is not a simple replacement of GPUs by ASICs but a more specialized coexistence. The consensus points toward "GPUs for training, ASICs for inference," or hybrid clusters. Ultimately, the rise of ASICs represents a democratization of computing power, shifting definition authority from a single chip giant to a broader ecosystem of cloud providers and end-users, offering the industry more choice in the silicon that powers AI.

marsbit05/18 00:29

This Chip Sector Is on Fire

marsbit05/18 00:29

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