The latest AI news, in focus.

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 mattersThis 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.

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 mattersThe 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.

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.

Why it mattersThe 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.

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.

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.

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.

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.

Why AI Compute Comes in Rectangles — Erik Bernhardsson, Modal CEO

Elastic GPUs, sandbox growth, and the economics of the agent cloud

Why AI Compute Comes in Rectangles — Erik Bernhardsson, Modal CEO

Elastic GPUs, sandbox growth, and the economics of the agent cloud

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.

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.

TorchNeuron System-Level Performance Analysis (Part 1): End-to-End Profiling Based on the AWS Trainium Chip

This article is the first in a series of technical blog posts jointly published by Hugging Face and Amazon, systematically introducing how to use TorchNeuron on AWS Trainium AI accelerator chips for PyTorch model inference performance analysis. It covers the principles of Neuron backend integration, methods for configuring torch.profiler, the use of NeuronConfig and NeuronProfiler tools, and visualized tracing with Neuron Explorer. Additionally, using RMSNorm as an example, it compares the device‑time differences among three implementations: plain PyTorch, the built-in torch.rms_norm fusion, and torch.compile’s automatic fusion.

i14 releases the Laplace 3B linear attention model: inference with fixed-state memory, implemented on AWS Trainium2 and native PyTorch.

i14 has launched the Laplace 3B model with 3.028 billion parameters, which replaces Softmax attention with Gated Linear Attention (GLA). During inference, its per-sequence recursive state remains constant at 29.5 MB, independent of context length. The model was trained and validated on a single AWS trn2.48xlarge instance (equipped with 16 Trainium 2 chips), achieving a training throughput of 120,000 tokens per second and an inference throughput of 11,544 output tokens per second (with 1,280 concurrent sequences). All performance metrics are based on internal measurements conducted by i14 and the AWS GenAIIC team.

SF Compute builds a programmable data center: leveraging physical delivery to power the GPU computing power trading market.

SF Compute was founded by Evan Conrad and is dedicated to building a neutral, programmable GPU computing‑power trading infrastructure that supports physical delivery. Its model rejects the abstraction of FLOPS as a commodity, emphasizing hardware heterogeneity, actual delivery, and real‑time constraints—such as interrupt tolerance, InfiniBand topology, and firmware versions. The platform has already facilitated tens of billions of dollars in transaction volume and introduced low‑level capabilities like dynamic InfiniBand subnet partitioning.

Visiting Samsung’s Paju Ultra-Large-Scale Semiconductor New Facility: The AI-Driven Surge in Memory Chip Capacity Expansion

To meet the surging demand for high-bandwidth memory chips driven by the explosive growth of generative AI, Samsung is building a semiconductor manufacturing cluster in Pyeongtaek, South Korea, spanning an area equivalent to New York’s Central Park. To date, three fabs have been commissioned, with a fourth set to begin production soon, and construction on a fifth—halted in 2024 due to declining demand—has now resumed. The site showcases an intense construction pace and a workforce ecosystem adhering to stringent safety standards.

Hugging Face launches TorchTPU: PyTorch natively supports Google TPU

Hugging Face has released TorchTPU, providing native TPU backend support for PyTorch, enabling libraries such as Transformers and Diffusers to run directly on TPUs without relying on torch_xla or migrating to JAX.

TorchNeuron launches public beta: Hugging Face models natively support AWS Trainium for training and inference.

Hugging Face, in collaboration with AWS, has launched TorchNeuron—a PyTorch-native Trainium backend—available for public beta starting October 20, 2026. This technology enables mainstream libraries such as Transformers and Diffusers to run directly on Trainium chips without any modifications, significantly enhancing the usability and compatibility of text-generation and image-generation models.

Impactful scheduling for GPU clusters

Impactful scheduling for GPU clusters

Import AI 470: No rights for machines; automating environment generation with SPADE; and building better GPU kernels with Hawkeye

Differential acceleration of cyber, math, and AI

GPU Management: Why Idle GPUs Are the New Grounded Aircraft

GPU Management: Why Idle GPUs Are the New Grounded Aircraft