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Marvell’s early chipmaking gamble pays off amid AI demand

Eight years ago, chipmaker Marvell Technology Inc. decided to play leapfrog with nanometers, and its trajectory has been steadily upward since. In semiconductor manufacturing, nanometer designations identify generations of chipmaking technology, known as process nodes, rather than the exact physical dimensions of transistors. Advanced process nodes generally enable greater transistor density and improvements in performance […] The post Marvell’s early chipmaking gamble pays off amid AI demand appeared first on SiliconANGLE.

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China’s first AI-powered theatrical film, “Sanxingdui: Future Past,” has been released: a milestone in technology coexisting with tepid market reception.

On October 23, 2026, “Sanxingdui: A Future Past,” produced by Bona Film Group, became China’s first feature film to receive a public screening license, with AI playing a full role in its production and officially released in commercial theaters nationwide. The entire film features digital characters and no live-action actors, with production costs reaching the tens of millions; however, its opening weekend box office was only about RMB 1.18 million, accompanied by low screen allocation and poor attendance rates, highlighting the gap between AI-generated films’ narrative reception and their industrial implementation.

Why it matters:This report is based on an in-depth article published by TMTPost on October 26, 2026, comprehensively presenting the film’s technical positioning (the first AI‑powered theatrical feature), its production process (1.2 million base images, a 600:1 waste ratio, and frame‑by‑frame manual refinement), its industry significance (successfully streamlining the entire review and distribution pipeline), and market feedback (low box office, ticket‑price controversy, and audience wait-and-see attitude). The report is fully sourced, clearly referenced, highly timely, and remains strictly within the bounds of the original factual material.

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The compilation rules for four indices, including the Shanghai Composite Index, will be adjusted starting today; the State-owned Assets Supervision and Administration Commission has outlined plans to deepen state-owned enterprise reform; S&P’s new regulations will affect $375 billion in funds.

Starting October 26, 2026, the SSE Composite Index, the SSE A‑Share Index, the SSE Free‑Float Index, and the STAR 50 Index will officially implement their revised compilation methodologies, introducing a new cap of 15% on the weight of any single constituent. On October 23, the State-owned Assets Supervision and Administration Commission of the State Council emphasized the need to steadily advance the implementation of the latest round of state‑owned asset and state‑owned enterprise reform tasks. On the same day (October 25), S&P Dow Jones Indices announced adjustments to its growth/value stock classification criteria; the new rules will take effect on December 21, impacting approximately $375 billion in passive investment funds.

Why it matters:The three policy and market‑rule changes all exhibit significant timeliness and systemic impacts: the index‑weighting red line directly affects the stability of A‑share core benchmarks; the SASAC’s statement reflects the central government’s urgent plan to tackle tough challenges in state‑owned enterprise reform; and S&P’s rule overhaul reshapes the logic of value investing, with implications amounting to hundreds of billions of dollars—making it a key variable in the global capital markets. All information is drawn from an authoritative, comprehensive report published by TMTPost on October 25, 2026, with clearly verifiable timelines, entities, measures, and impact figures.

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Panel manufacturers are collectively raising prices to cope with upstream cost pressures driven by AI, but downstream acceptance remains low, putting pressure on end-product quality control.

Starting in September 2026, panel manufacturers such as TCL CSOT, BOE, and HKC have issued price‑increase notices one after another. The primary reason is the rapid growth of AI large models, which has led to a sharp surge in demand for HBM, squeezing capacity for mature‑process chips and driving up the prices of key components like driver ICs, PCBs, and glass substrates. Upstream suppliers, including Corning, have also raised their prices in tandem. Despite clear cost pressures, downstream TV and monitor brands have yet to broadly increase purchase prices, while end users continue to report frequent quality‑control issues such as overheating and black spots. As a result, these price hikes have not yet translated into actual sales or improved user experiences.

AI大模型狂飙,面板行业也扛不住了

Why it matters:The materials system reveals the cross‑chain transmission mechanism through which the AI industry impacts indirectly related hardware sectors—rather than driving panel demand directly, it triggers a full‑chain cost repricing via the reallocation of wafer‑fabrication capacity. At the same time, it highlights a significant gap between price‑increase behavior and actual market feedback (price rigidity, declining shipments, and user complaints), underscoring the deep‑seated contradiction between industry bargaining power and product competitiveness.

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Intel has received an exemption from the U.S. FCC’s ban on foreign-made robots, in order to ensure automated logistics at its chip fabs.

On October 22, 2026, the U.S. Federal Communications Commission (FCC) granted Intel a one-year exemption, allowing it to continue using certain foreign‑made automated guided vehicles (AGVs) at its U.S. semiconductor fabs, as the original ban’s vague wording could inadvertently affect critical semiconductor manufacturing equipment.

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Kazakhstan is accelerating the development of its AI computing infrastructure, and Firebird has obtained U.S. export approval to deploy 45,000 NVIDIA Grace Blackwell GPUs.

Kazakhstan’s AI ecosystem is transitioning from the planning phase to large-scale deployment. Firebird has obtained U.S. export licenses to build an AI factory in Kazakhstan, with the first phase deploying 45,000 NVIDIA Grace Blackwell GPUs. Kazakhtelecom has already brought online more than 1,000 H200/B300 GPUs and plans to add over 2,000 Vera Rubin and other GPUs by 2025. Multiple stakeholders are collaborating to advance computing power supply, developer access, and the local implementation of AI applications.

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Amazon SageMaker AI has launched GPU-free managed model training sessions, now generally available.

AWS has announced that SageMaker AI’s serverless model customization feature now includes “Training Sessions,” which are fully available starting today. Users can independently define post-training logic—such as custom loss functions, reward signals, or multi‑round reinforcement learning—while the platform automatically handles GPU allocation, distributed execution, fault tolerance, and checkpoint storage.

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Amazon SageMaker launches the Training Session API: users can write custom training loops for large models without managing GPUs.

Amazon SageMaker has released the Training Sessions API, enabling users to implement custom large-model training workflows—such as SFT, RLHF, GRPO, and KTO—using pure Python scripts. GPU resource scheduling, fault tolerance, distributed execution, and checkpoint management are all handled by the service.

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Jas Kaira, Head of AI Business at Blackstone: Concerns about AI infrastructure debt are exaggerated.

Jas Kaira, head of Blackstone’s AI business, stated at the TechCrunch Disrupt conference that market panic over massive debt in the AI infrastructure sector has been overly amplified. His key argument is that this debt is largely backed by long-term contracts with high‑credit‑rating clients—such as Microsoft and Meta—and that the underlying assets (e.g., GPUs) possess strong collateral value, meaning the risk profile differs fundamentally from that of the 2008 banking system.

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NVIDIA cuDSS officially released: Accelerating the solution of sparse linear systems in engineering and scientific simulations

On October 26, 2026 (UTC), NVIDIA announced that the cuDSS library has entered General Availability (GA). This CUDA-X library is specifically designed to efficiently solve large-scale sparse linear systems on GPUs, significantly boosting simulation throughput and iterative efficiency across fields such as chip design, aerospace, and industrial systems.

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