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McDonald's is facing a proposed nationwide class-action lawsuit in the United States, with plaintiffs alleging that the fast-food giant used an artificial intelligence pricing system to illegally coordinate menu prices.

The suit was filed last Friday (October 2) in federal court in Chicago. The complaint claims McDonald's conspired with independent franchisees to manipulate prices using algorithms trained on non-public transaction data, violating U.S. antitrust laws. Previous Reuters reporting indicated that McDonald's pricing engine analyzes millions of daily transactions across nearly 14,000 locations.

In a statement released Monday, McDonald's rejected the allegations as subjective speculation without factual basis. The company asserted, "No Big Mac or any other item's price is set by AI," emphasizing that franchisees retain pricing authority and that data analytics tools are widely used across industries for recommendations.

This case follows recent U.S. lawsuits against hotels and apartment rentals over algorithmic price coordination. If courts ultimately rule that algorithmic outputs constitute illegal collusion, it could have significant legal implications for large chain enterprises relying on dynamic pricing.

OpenAI is introducing visual display ads that will appear alongside images generated by ChatGPT.

The feature begins rolling out in the U.S. later this month, targeting only free and low-cost subscription tiers. The company states that ads will be clearly labeled and will not influence the answers provided by ChatGPT.

This move aims to attract online advertisers looking to use visual storytelling to reach potential customers. Eventually, these ads are expected to target ChatGPT’s 1.2 billion weekly users as the rollout expands globally.

German industrial robotics startup RobCo announced the closure of a $40 million funding round, pushing its valuation above $1 billion.

The transaction primarily involved current and former employees selling shares to investors including Sequoia and Lightspeed. This represents a doubling of the company's value compared to January.

RobCo specializes in logistics and manufacturing automation, featuring the Alfie mobile robot and RobVision AI engine. The capital will support CEO Roman Hölzl's move to San Francisco to expand the company's U.S. presence.

Nolla Health's AI system has received approval in Utah to autonomously issue acne prescriptions.

While previous AI medical pilots were limited to medication renewals, this is the first case in the US approved for issuing "initial" prescriptions. The pilot gradually reduces human oversight: the first 100 cases require dual physician review, shifting to sample audits thereafter.

The service costs $4.99/month and is restricted to Utah residents aged 18+ with mild-to-moderate acne. If the AI cannot confidently select a treatment, users are referred to a human physician.

Reflection AI released its first open-weight model, Beam, on October 5, positioning it as a "workhorse" for Western enterprises.

The model uses a sparse Mixture-of-Experts architecture with 501 billion total parameters and 23 billion active. CEO Misha Laskin stated that Beam excels in coding and agentic tasks, requiring three to four times less computing power for reasoning than comparable open models.

Official benchmarks indicate Beam's performance is comparable to China's Z.ai GLM-5.2 and approaching Alibaba's Qwen 3.8-Max. The launch targets businesses and governments seeking sovereign AI options without relying on Chinese infrastructure.

The model is currently undergoing final red-teaming and evaluations, with weights and technical reports scheduled for release later this month.

Microsoft and Meta are significantly reducing their internal use of Anthropic’s Claude models, according to a report by The Decoder citing The Information.

At Microsoft, annual Claude spending had been projected at over $1 billion. Executives Scott Guthrie and Jay Parikh have reportedly instructed employees to switch to in-house tools like GitHub Copilot and OpenAI models. The monthly per-employee budget in the cloud division dropped from $100,000 to approximately $10,000, representing a spending cut of more than one-third.

Meta has seen its Claude Code user base fall from roughly 60,000 to 30,000. This decline is partly due to layoffs but also reflects Meta’s push for its own tools, such as Muse Code and MetaCode. In a recent 28-day period, Meta spent over $105 million on Claude Code alone.

This move highlights a broader trend among large tech companies to reduce dependency on external model providers and strengthen proprietary AI infrastructure. For Anthropic, the contraction of its core customer base poses potential challenges to its enterprise revenue growth.

Instinct announced on October 5 that it is bringing its AI agent into group chats, enabling participation even from friends who do not have an Instinct account.

The feature allows users to add the agent to conversations to help with tasks like travel planning, carpooling, or event coordination. Founder Noah Shinn stated on X that personal agents are siloed from group agents, which cannot access personal data. The system requires explicit user permission before sharing information or taking action.

This move targets the consumer AI market, competing directly with rivals like Meta’s Muse. The feature is currently rolling out to early access users, with a full rollout timeline unspecified.

Amazon AWS hired Pedram Rezaei as a Distinguished Engineer on October 5, joining its Agentic AI and Emerging Technologies group.

Rezaei spent over two decades at Microsoft, most recently serving as Copilot's CTO until June and working on foundation model training within the Superintelligence team. He will initially focus on bolstering Quick, AWS's AI assistant, and Kiro, its AI-powered coding tool. Internal emails note rapid growth for these products: Kiro's user base doubled in Q2, while Quick's monthly AI users grew fourfold.

This hire signals Amazon's strategy to acquire top-tier technical talent from competitors to accelerate the deployment of widely used AI agent products.

Ghost announced its emergence from stealth today, completing an $11 million seed round led by Andreessen Horowitz.

Its first product, Core, is a personal computer designed specifically for running AI agents, priced at $3,499. The device includes an Nvidia RTX Pro 4000 SFF Blackwell GPU and comes pre-installed with models like Qwen and Gemma, aiming to let users process data and execute tasks locally without relying on cloud services.

The company states that all source code, logic, and model weights reside on the device, giving users full control. Pre-orders opened Monday, with shipping scheduled for the last week of October.

CoreWeave Senior Director Harsh Banwait reported that Nvidia's Vera CPU improved Sandboxes startup times by approximately 3x.

Sandboxes provides isolated environments for AI agents to execute tasks during reinforcement learning and inference. Banwait noted this addresses the bottleneck of waiting for environment initialization before agent task execution.

The data comes from CoreWeave's internal testing and has not been published with detailed benchmarks or independently verified.

Most Americans want AI development to "slow down."

According to a Quinnipiac University poll released on September 30, 77% of US respondents want to reduce the pace or fully stop AI development until its safety has been verified. Only 5% favor faster progress.

Public trust in AI companies remains low. The survey found that 86% support independent safety standards, even if they hinder innovation; meanwhile, 74% express little or no trust in the leaders of AI companies. Researcher Chetan Jaiswal noted that these results suggest the public wants safeguards that do not depend simply on taking company leaders at their word.

CoreWeave announced Model Distillation, RL Rollouts, and a programmable training API on October 5 to close the loop between inference and post-training.

Traditionally, model improvement halts at deployment. These tools allow teams to capture production traffic failures as training data automatically, bypassing manual infrastructure setup.

In a cited example, customer Method used GPT-4o outputs to train a Llama 3.1 8B model for bank IVR systems. After iterating with fresh production data via the new workflow, the updated model won 57.5% of head-to-head evaluations against the previous production version. Another customer, Willow, combined supervised learning and reinforcement learning to improve text styling.

Note that these capabilities are largely in preview or newly launched states. Performance depends on user-specific data quality, and the cited metrics are vendor-reported without independent verification.