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The U.S. Census Bureau has made a significant wording change to a key question in its bi-weekly Business Trends and Outlook Survey (BTOS). This shift stems from Executive Order 14434, signed by the U.S. President, which aims to inaugurate an era of "Super Intelligence."

According to an analysis of the latest survey documents by developer Simon Willison, starting with the collection cycle on October 19, 2026, the question regarding artificial intelligence usage was changed from "In the last two weeks, did this business use Artificial Intelligence (AI)..." to "In the last two weeks, did this business use Super Intelligence or SI (formerly referred to as Artificial Intelligence or AI)...". The examples provided also shifted from general terms like machine learning and natural language processing to politically charged phrases such as "SI-powered data analysis" and "generative SI for marketing."

This change directly impacts data comparability. Previously, the question had been broadened in November 2025 from "producing goods or services" to "any business functions," causing the percentage of affirmative responses from large companies to jump from around 12% in September 2025 to 30% by February 2026, and reaching approximately 40% by October 2026. However, with the introduction of the new "Super Intelligence" wording, currently published data still reflects the old "Artificial Intelligence" definition, while the first results incorporating the new wording are expected to be released the following Thursday. This means that in the short term, the official tracking of U.S. corporate AI adoption via BTOS will suffer from definitional discontinuity or gaps.

For investors and researchers monitoring AI industry penetration and macroeconomic impact, this event highlights how non-market factors can distort core statistical indicators. When official definitions are replaced by politicized terminology, historical trend lines may become invalid, and new baselines require time to establish. Readers should treat recent conclusions drawn from BTOS data about "accelerating" or "slowing" AI adoption with caution, awaiting the release of the first batch of data under the new criteria to recalibrate expectations.

Match Group announced during its third-quarter 2026 earnings call that it is testing a cheaper subscription plan for its dating app Hinge. The move aims to convert more users into paying customers as people grow increasingly tired of expensive dating app subscriptions. Steve Bailey, Chief Financial Officer of Match Group, stated the new tier is designed for women and price-sensitive users, offering features like additional filters and custom location boundaries.

The decision highlights a strategic tension within Match Group’s portfolio. According to Bailey, Hinge is one of the company’s highest-priced products, with revenue per payer at approximately $30—nearly twice that of Tinder. However, Hinge’s payer penetration is only slightly more than half that of Tinder. While Hinge reported strong growth in Q3 2026, with global monthly active users up 12% year-over-year and direct revenue increasing 19%, its paid subscriber count stands at 2.3 million. In contrast, Tinder generated $480 million in direct revenue but saw a slight 2% decline year-over-year, with 8.9 million paid users.

This shift marks a reversal from Hinge’s previous strategy. In 2023, the company experimented with a more expensive tier aimed at users willing to pay extra for advanced features. Now, facing broader subscription fatigue, Match Group is exploring whether a more affordable option can appeal to a wider audience without diluting the brand’s premium positioning. The exact price of the new tier remains undisclosed.

CircuitHub, a printed circuit board (PCB) manufacturer based in Massachusetts, USA, claims that its robotic manufacturing system called 'The Grid' can complete certain orders faster than its Chinese competitors. The company states that demand is already well ahead of what it can build and plans to quintuple its capacity this year.

'The Grid' is described as 'one large robot,' replacing conventional fixed production lines with coordinated machinery and mobile platforms transporting boards between manufacturing stations. Each board follows its own route, allowing different products to undergo manufacturing simultaneously without conventional production-line changeovers. The system handles 100 different designs using shared manufacturing equipment.

CircuitHub compared its factory with a typical quick-turn manufacturer in Shenzhen, China, using an example involving five assembled motor-controller boards. Producing these boards through 'The Grid' costs $630, with delivery to a United States address taking four days. The equivalent Shenzhen order costs approximately $465 after shipping expenses and United States import tariffs, with median delivery taking nine days. The American factory therefore charges approximately $165 more, although customers receive their completed circuit boards five days earlier.

CircuitHub argues that the shorter delivery period gives engineering teams additional time to continue development rather than waiting for foreign manufacturing. However, this comparison comes from CircuitHub and concerns one particular order, rather than independently established performance across different manufacturers.

Direct Numerical Simulation (DNS) is the most accurate method for simulating turbulence in computational fluid dynamics (CFD), solving all scales of fluid motion without relying on turbulence models. However, its extreme demand for memory and compute resources often limits its application to smaller cases or requires specialized high-performance computing infrastructure.

CoreWeave, an AI infrastructure provider, partnered with Convergent Science, a CFD software developer, to benchmark CONVERGE CFD software on NVIDIA HGX B200 GPU clusters. The test successfully completed a DNS case containing 536 million grid cells in 3.62 hours using two NVIDIA HGX B200 instances, comprising 16 NVIDIA Blackwell GPUs.

The study also compared two distinct hardware classes: NVIDIA RTX PRO 6000 Blackwell Server Edition and NVIDIA HGX B200. The RTX PRO server is suited for everyday industrial simulations that fit within a single node, utilizing PCIe for intra-node communication. In contrast, the HGX B200, with significantly higher memory bandwidth (~7.7 TB/s vs 1.6 TB/s) and NVLink interconnects, is designed for ultra-large cases spanning multiple nodes.

It is important to note that these results come from a vendor-partnered benchmark designed to demonstrate theoretical upper limits and scaling characteristics under specific conditions. Real-world production performance may vary depending on physical models, boundary conditions, and software optimizations.

According to a report by South Korean media outlet Byline Network, Viva Republica, the operating entity behind the fintech giant Toss, is planning to enter the Japanese securities market. The company intends to acquire 100% of the shares of Cher Securities, a mobile-specialized brokerage under the Tokai Tokyo Financial Group, through its overseas business arm, Toss Global. The transaction value is estimated at approximately 4 billion yen (around 34 billion won), with a target completion date by March 2027.

Cher Securities, established in 2019, is an online brokerage focused on small-scale stock trading and investment trust services via smartphones. Toss stated that the decision to acquire Cher Securities stems from the alignment of their service philosophies, particularly the focus on accessibility for beginner investors. The strategy involves leveraging Cher’s existing customer base and operational experience while integrating Toss’s mature mobile trading system (MTS/WTS) technologies to gradually enhance services without aggressive restructuring.

This strategic move reflects Toss’s search for new growth engines. While Toss boasts a massive user base in South Korea, its profitability relies heavily on its domestic securities business, where growth margins are tightening. Compared to the fierce competition and strict regulatory barriers faced by foreign entrants in Japan’s banking sector, the securities industry offers a more realistic entry point. Japan’s relatively stable political environment and well-established financial regulatory framework are also viewed as favorable conditions for testing digital financial platform models.

For investors, this acquisition represents a critical step in Toss’s globalization strategy. Previously, Toss launched its eponymous app in Australia via Toss Global. If the integration in Japan proceeds smoothly, it could contribute direct overseas revenue and add a significant “cross-border platform capability” narrative to the company’s potential IPO valuation. However, the deal is not yet finalized and remains subject to approval by the Japanese Financial Services Agency (FSA) and subsequent integration risks.

British high-performance sports car manufacturer McLaren Automotive has announced a partnership with engineering computing platform Rescale and chipmaker NVIDIA to integrate Agentic AI and Physics AI into its vehicle engineering development process. This initiative aims to address the core bottleneck of traditional Computer-Aided Engineering (CAE): high computational costs that limit the scale of design exploration.

In conventional automotive engineering, evaluating body structure stress, dynamic performance, and material durability requires running complex numerical simulations. While these high-fidelity simulations provide reliable physical evidence, each run consumes significant time. When engineers need to repeatedly adjust parameters or compare numerous design options, computational resources become a constraint, limiting the number of designs assessable within the development cycle. For high-performance vehicles requiring precise tuning, accelerating iteration while maintaining analytical reliability is a critical challenge.

McLaren's solution combines "Surrogate Models," a type of AI that learns physical system behavior from existing simulation or experimental data to map relationships between design parameters and physical outcomes. Instead of re-running high-precision numerical simulations every time, surrogate models can predict results under different design conditions at millisecond speeds within their applicable range. According to data provided by Rescale, McLaren engineers can now evaluate thousands of design variations in hours, compressing complex simulation tasks that previously took days into minutes. This mechanism allows teams to first screen out designs worth deeper analysis using low-cost predictions, then perform high-precision validation on key candidates.

Beyond Physics AI, the platform introduces AI Agents to handle repetitive engineering tasks. Rescale's platform integrates agentic engineering technologies into McLaren's R&D environment, automating the chaining of simulation and analysis workflows. This frees engineers to focus more on design judgment and complex problem analysis. Additionally, the platform uses machine learning to predict manufacturing performance for carbon fiber structures and organizes past project insights via engineering knowledge graphs to support subsequent decisions.

Notably, Rescale claims the platform can boost professional engineering productivity by up to 3x, though public materials do not detail the measurement methodology, sample scope, or independent verification methods for this figure. Surrogate models do not fully replace traditional CAE; their predictive capability depends on training data and applicability ranges, meaning critical designs still require high-precision simulation confirmation. This case demonstrates the practical application value of AI in industrial R&D, extending beyond "code generation" to "physical simulation acceleration."

U.S. technology company Apple disclosed in its latest fiscal fourth-quarter results that its inventory levels rose significantly, growing from $11.09 billion in Q3 to $14.54 billion in Q4, a 31.1% increase quarter-over-quarter. Compared to $5.72 billion in the same period of 2025, inventory surged 154.3% year-over-year, reflecting the company's stockpiling strategy amid tight component supplies.

Apple CEO John Ternus stated during the earnings call that the current situation with memory pricing is "unprecedented." He noted that while carrying inventory and lower costs on certain non-memory components partly offset higher memory costs seen since June, the company expects even higher memory costs in the December quarter. Consequently, Apple lowered its gross margin guidance for the next quarter to between 45% and 46%, down from 47.9% in the September quarter.

Ternus also revealed that Apple's manufacturing purchase obligations increased by $33 billion from June to the September quarter as the company works to improve supply. Chief Operating Officer Sabih Khan joined the discussion on memory issues, reiterating the increased purchase obligations and not ruling out future price increases on devices. Additionally, weak iPad sales were attributed to supply constraints, with revenue at $6.2 billion, below analyst expectations of $6.7 billion and down 10% year-over-year.

US-based AI company Anthropic has updated the system prompt for its coding tool, Claude Code, to explicitly prevent it from being used to recreate specific existing video games. The change was confirmed by Anthropic engineer Addy Osmani on November 2, 2026, aiming to curb growing issues with AI-generated game infringement.

Osmani stated that two key sentences were added to the prompt for the Opus 5.5 model: “Do build original games, games in a genre, and games from the user’s own designs and assets. Don’t build a recreation of a specific existing game, even if it is named or called original.” This follows reports from Tom's Hardware and screenshots shared by users on social media highlighting the new restrictions.

While direct cloning is blocked, Anthropic emphasized that users can still create original games, work within specific genres, or use their own assets. Osmani noted that this aligns with the company's broader usage policy against intellectual property infringement. He indicated that Anthropic would continue to iterate on these guidelines to balance creative freedom with legal compliance, inviting users to report false positives where the AI refused legitimate requests.

As of the close on November 2, 2026, the tech-heavy Nasdaq 100 Index has fallen 10% from its peak in October, officially entering a technical correction. The S&P 500 Index declined approximately 4% over the same period.

The Philadelphia Stock Exchange Semiconductor Index (SOX) dropped 3.4% on Monday, adding to Friday's 7.2% plunge. The chip-stock gauge failed to rise above its 200-day moving average on Monday after closing below that level for the first time since June 2025.

"Lower oil should be a tailwind for tech, but the fact that stocks could not hold the open even with crude plunging tells shows how little conviction is left even after last week’s carnage," said Dave Mazza, chief executive officer at Roundhill Financial.

Vercel has announced that its AI Gateway now supports the LongCat 2.5 model from Meituan. This model combines native visual understanding with coding and multi-step tool use capabilities, designed to assist agents working across complex codebases and full-project builds.

According to Vercel, LongCat 2.5 features improvements in visual programming, creative design, and 3D tasks, alongside longer agent workflows that carry engineering work from planning through execution. The model supports text and image inputs with a 1 million-token context window for handling code, documents, and extended task histories.

Developers can access the model by using meituan/longcat-2.5 as the model name. It is compatible with coding agents such as Claude Code, Codex, and Cursor, requiring the latest Vercel CLI installation and running npx vercel ai-gateway setup for configuration.

Vercel AI Gateway provides a unified API for calling models, tracking usage and cost, and configuring retries, failover, and performance optimizations. The gateway reflects provider pricing with no markup and does not charge a platform fee on inference, including for Bring Your Own Key (BYOK) requests.

Configuring Kubernetes for new GPU hardware is a process prone to errors. Drivers, kernels, container runtimes, networking, operators, and schedulers must work in concert, with settings tuned specifically to the hardware and workload. Even minor mismatches can prevent workloads from running or limit their performance. In a technical blog post dated November 2, 2026, NVIDIA cloud architect Mark Chmarny and software engineer Yuan Chen detailed how they completed this adaptation on early Vera Rubin NVL72 hardware and solidified their experience into versioned recipes within the NVIDIA AI Cluster Runtime (AICR).

The upgrade from GB200 NVL72 to Vera Rubin NVL72 involves more than fine-tuning; user-visible changes are significant. The GPU driver requires a new branch: the validated R615.23 is an open-source kernel module driver pre-installed on the host, while the official platform support target is R620. On the network side, ConnectX-7 NICs and BlueField-3 DPUs are replaced by ConnectX-9 SuperNICs and BlueField-4 DPUs, with DOCA 3.3.0 being the first GA release supporting ConnectX-9. The device topology also differs: the Vera Rubin NVL72 compute tray exposes eight groups of ConnectX-9 PCI devices totaling 32 functions, whereas the GB200 has only four ConnectX-7 adapters. Multi-node NVLink workloads still rely on IMEX, but DRA-managed IMEX is mutually exclusive with the host's static nvidia-imex service, which must be disabled.

These changes are scattered across various layers, from low-level bindings to the orchestration layer. AICR addresses this by encoding all findings into recipes: host-managed mode drivers (GPU Operator sets driver.enabled: false, while the container toolkit remains operator-managed), DRA drivers enabling ComputeDomains, a minimum Kubernetes version of 1.34.1 (1.34 provides the GA resource.k8s.io/v1 DRA API, and the .1 patch is required because the RKE2 CDI/NRI toolkit path needs containerd 2.1), Ubuntu 26.04 paired with a Vera-optimized arm64 64k-page kernel, and raising the NFD topology updater's default 60Mi memory limit—otherwise, it triggers OOM kills on multi-TB NVL72 worker nodes. The recipe also introduces DraNet for allocating ConnectX-9 devices to Pods and employs NodeWright to enforce an exclusive RDMA network namespace mode, ensuring each Pod sees only its own Host Channel Adapter.

The validation process executes in four steps on a live Vera Rubin NVL72 reference cluster. Step one generates the recipe, offline parsing 14 components and 5 overlay layers. Step two renders the deployment bundle, producing 61 files including ordered Helm charts and deployment scripts. Step three snapshots the cluster and runs validation: the deployment phase passes all four checks; the conformance phase passes eight, covering GPU Operator health, DRA/ComputeDomain support, KAI gang scheduling, Pod autoscaling, and secure accelerator access. Step four is the cross-node ComputeDomain test, verifying IMEX channel injection and NVLink fabric connectivity. The performance phase runs a cross-node NCCL all-reduce, completing a 16 GiB message all-reduce over the NVLink fabric via IMEX ComputeDomain, achieving a bus bandwidth of 744.71 GB/s, meeting the recipe's floor. The entire sequence (recipe, bundle, snapshot, deployment, and conformance validation) completes in about five minutes, with the performance phase adding the runtime of the NCCL job itself.