California has entered a new era of privacy and AI enforcement after the state’s privacy regulator fined Tractor Supply USD1.35 million for failing to honour opt-outs and ignoring Global Privacy Control signals. The case marks the largest penalty yet from the California Privacy Protection Agency.
In California, there is a widening focus on how companies manage consumer data, verification processes and third-party vendors. Regulators are now demanding that privacy signals be enforced at the technology layer, not just displayed through website banners or webforms.
Retailers must now show active, auditable compliance, with clear privacy notices, automated data controls and stronger vendor agreements. Regulators have also warned that businesses will be held responsible for partner failures and poor oversight of cookies and tracking tools.
At the same time, California’s new AI law, SB 53, extends governance obligations to frontier AI developers, requiring transparency around safety benchmarks and misuse prevention. The measure connects AI accountability to broader data governance, reinforcing that privacy and AI oversight are now inseparable.
Executives across retail and technology are being urged to embed compliance and governance into daily operations. California’s regulators are shifting from punishing visible lapses to demanding continuous, verifiable proof of compliance across both data and AI systems.
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PayPay, Japan’s top cashless payment firm and a SoftBank company, has acquired 40% of Binance Japan to unite traditional finance with blockchain innovation. The partnership merges PayPay’s 70 million users and trusted network with Binance’s digital asset expertise and global Web3 leadership.
Under the new alliance, Binance Japan users will soon be able to purchase cryptocurrencies using PayPay Money and withdraw funds directly into their PayPay wallets. The integration seeks to simplify digital trading and connect cashless payments with decentralised finance.
Executives from both companies highlighted the significance of this collaboration. PayPay’s Masayoshi Yanase said the deal supports Japan’s financial growth, while Takeshi Chino called it a milestone for everyday Web3 adoption.
The alliance is expected to accelerate Japan’s digital finance landscape, strengthening its role as one of the world’s most advanced economies in financial technology. By combining secure payments with blockchain innovation, PayPay and Binance Japan aim to build a seamless digital economy.
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Ant Group has unveiled its Ling AI model family, introducing Ling-1T, a trillion-parameter large language model that has been open-sourced for public use.
The Ling family now includes three main series: the Ling non-thinking models, the Ring thinking models, and the multimodal Ming models.
Ling-1T delivers state-of-the-art performance in code generation, mathematical reasoning, and logical problem-solving, achieving 70.42% accuracy on the 2025 AIME benchmark.
A model that combines efficient inference with strong reasoning capabilities, marking a major advance in AI development for complex cognitive tasks.
Company’s Chief Technology Officer, He Zhengyu, said that Ant Group views AGI as a public good that should benefit society.
The release of Ling-1T and the earlier Ring-1T-preview underscores Ant Group’s commitment to open, collaborative AI innovation and the development of inclusive AGI technologies.
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Amazon has launched Quick Suite, an AI-powered workspace that delivers fast insights and simplifies business operations. The platform gathers data from Slack, Salesforce, Snowflake, other databases, and the public internet, providing answers from single points to full research projects.
Quick Suite enables users to move seamlessly from insights to action, integrating with applications such as Salesforce, Jira, and ServiceNow. It automates tasks from daily routines, like responding to RFPs, to complex processes such as invoice handling, while keeping all data secure and private.
The platform is simple to deploy, with AWS administrators able to activate it in a few steps. New customers get a 30-day free trial for up to 25 users, with Quick Suite available in key AWS regions and expansion planned.
Quick Suite aims to enhance productivity and decision-making by combining AI-driven research, chat, and automation capabilities in one workspace, enabling businesses to act on insights faster than ever before.
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Google is seeking permission to bundle its Gemini AI application with long-standing services such as YouTube and Maps, even as US regulators press for restrictions to curb its dominance in search.
At a recent court hearing, Google’s lawyer John Schmidtlein told Judge Amit Mehta that tying Gemini to its core apps is vital to delivering a consistent AI experience across its ecosystem.
He insisted the courts should not treat the AI market as a settled domain subject to old rules, and claimed that neither Maps nor YouTube is a monopoly product justifying special constraints.
The government’s position is more cautious. During the hearing, Judge Mehta questioned whether allowing Google to require its AI app to be installed to access Maps or YouTube would give it unfair leverage over competitors, mirroring past practices that regulators found harmful in search and browser markets.
This moment frames a broader tension: how antitrust frameworks will adapt (or not) when dominant platforms seek to integrate generative AI across many services. The outcome could shape the future of bundling practices and interoperability in AI ecosystems.
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Researchers at MIT’s Computer Science and AI Laboratory have developed a new AI system that can build realistic virtual environments for training robots. The tool, called steerable scene generation, creates kitchens, restaurants and living rooms filled with 3D objects where robots interact with the physical world.
The system uses a diffusion model guided by Monte Carlo tree search to produce scenes that follow real-world physics. Unlike traditional simulations, it can accurately position objects and avoid visual errors such as items overlapping or floating unrealistically.
By generating millions of unique, lifelike environments, the system can dramatically increase the training data available for robotic foundation models. Robots trained in these AI settings can practise everyday actions like stacking plates or placing cutlery with greater precision.
The researchers say the technique allows robots to learn more efficiently without the cost or limits of real-world testing. Future work aims to include movable objects and internet-sourced assets to make the simulations even more dynamic and diverse.
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Intel has revealed its next-gen client and server processors using Intel 18A, the most advanced US-made semiconductor node. The new Intel Core Ultra 3 (Panther Lake) and Xeon 6+ (Clearwater Forest) promise major performance and efficiency gains, with production already underway at Fab 52 in Arizona.
Panther Lake introduces a scalable multi-chiplet architecture for consumer and commercial AI PCs, gaming devices, and edge applications. It offers over 50% faster CPU and GPU performance, up to 180 TOPS for AI, and new robotics AI capabilities.
High-volume production begins later this year, with broad availability expected in January 2026.
Clearwater Forest is designed for hyperscale data centres, cloud providers, and telcos. Built entirely on Intel 18A, the Xeon 6+ processor offers up to 288 E-cores, a 17% IPC uplift, and improved density, throughput, and power efficiency.
It is set to launch in the first half of 2026.
Intel 18A underpins at least three upcoming generations of client and server products, with key innovations including RibbonFET transistors, PowerVia backside power delivery, and Foveros advanced packaging.
Fab 52 further strengthens the company’s US manufacturing leadership, supporting domestic production and strategic supply chain resilience.
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Microsoft Azure has launched the world’s first NVIDIA GB300 NVL72 supercomputing cluster, explicitly designed for OpenAI’s large-scale AI workloads.
The new NDv6 GB300 VM series integrates over 4,600 NVIDIA Blackwell Ultra GPUs, representing a significant step forward in US AI infrastructure and innovation leadership.
Each rack-scale system combines 72 GPUs and 36 Grace CPUs, offering 37 terabytes of fast memory and 1.44 exaflops of FP4 performance.
A configuration that supports complex reasoning and multimodal AI systems, achieving up to five times the throughput of the previous NVIDIA Hopper architecture in MLPerf benchmarks.
The cluster is built on NVIDIA’s Quantum-X800 InfiniBand network, delivering 800 Gb/s of bandwidth per GPU for unified, high-speed performance.
Microsoft and NVIDIA’s long-standing collaboration has enabled a system capable of powering trillion-parameter models, positioning Azure at the forefront of the next generation of AI training and deployment.
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Under the deal, Microsoft will pay a licensing fee to Harvard. The partnership aims to enhance Microsoft’s Copilot AI assistant by enabling it to provide medical advice that more closely aligns with what a user might receive from a healthcare professional.
So far, Copilot’s underlying models have been powered primarily by OpenAI. But this agreement is part of Microsoft’s broader push to diversify and reduce its dependence on OpenAI’s technology stack.
Dominic King, Microsoft’s vice president of health, has said the goal is for Copilot’s responses to health queries to more accurately reflect what a medical practitioner would say, rather than generic or superficial answers.
Microsoft declined to comment in detail, but the move strengthens Copilot’s differentiation in the health domain, arguably a high-stakes vertical area where accuracy and trustworthiness matter greatly.
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Nvidia is reportedly investing up to $2bn in Elon Musk’s AI company, xAI, as part of a $20bn funding round aimed at scaling its Colossus 2 data centre in Memphis. The capital will be used to buy Nvidia GPUs, essential for powering xAI’s next generation of AI models.
The funding package combines about $7.5bn in equity and up to $12.5bn in debt, structured through a special purpose vehicle that will lease the hardware to xAI over five years. The debt is secured by the GPUs themselves, allowing investors to recover their costs through chip rentals.
xAI faces mounting financial pressure, with reports indicating a cash burn of around $1bn per month. The firm raised $10bn earlier in the year and continues to draw on capital from Musk’s other ventures, including SpaceX.
The move comes amid an intense funding surge across the AI sector, as OpenAI, Meta and Oracle also announce multi-billion-dollar investments in infrastructure. Nvidia’s latest deal with xAI further cements its position at the centre of the global AI hardware ecosystem.
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