Microsoft is about to block or limit key Microsoft 365 functionality on older Mac and iPhone Office apps unless users update their operating systems or Office clients. Users running legacy Office builds (including older perpetual-license versions) or outdated macOS/iOS may find Microsoft 365 sign-ins, cloud syncing, and editing of OneDrive/SharePoint documents degraded — apps can revert to read-only access or lose real-time co-authoring and other integrated features. The change is driven by Microsoft’s requirement for modern authentication, newer APIs, and updated client capabilities; staying on unsupported OS or app versions will prevent access to many cloud-first features. To avoid disruption, update macOS/iOS and Office apps, switch to the Office web apps, or consult IT administrators about upgrade paths. The requirement also affects newer platform-dependent services such as AI-powered features (Copilot) and advanced collaboration tools, which will only work on supported clients.

The 'o1' model in ChatGPT features a hidden parameter in its Custom Instructions that allows users to adjust the reasoning effort of the AI. By explicitly directing the model to 'think longer' or 'deepen its reasoning process' within these instructions, users can significantly boost performance for complex programming tasks, logic puzzles, and nuanced analysis. Deep reasoning allows the o1 model to dedicate more time to scratchpad-style processing before providing an final output, reducing errors and improving the quality of its logic. This discovery highlights how users can exert more control over the model's 'chain-of-thought' capabilities, effectively forcing the AI to slow down and verify its steps, which leads to noticeably more accurate and comprehensive results compared to default settings.

Elastic has agreed to acquire CRV‑backed DeductiveAI for up to $85 million to accelerate its AI and data-product roadmap. The deal, reported by TechCrunch, is intended to fold DeductiveAI’s tooling and engineering talent into Elastic’s platform to enhance search, observability and AI-driven analytics across the Elastic Stack. DeductiveAI, backed by venture firm CRV, brings technology aimed at helping teams understand, debug and operationalize data-driven models and pipelines. Elastic expects the acquisition to strengthen its ability to offer customers tighter integrations between data ingestion, indexing and model-informed search/observability features, and to speed development of generative and retrieval-augmented capabilities on Elastic Cloud. Financial terms are structured as a headline value of up to $85M, likely including upfront consideration plus contingent payouts tied to milestones. Employees and stakes of DeductiveAI are expected to transition into Elastic, while the move positions Elastic more directly in the competitive space for AI-enabled infrastructure and observability tools.

Anthropic's update to Claude Code Artifacts introduces live, shared dashboards and interactive workspaces that let enterprise teams build, run and collaborate on model-driven applications and analyses in real time. The release emphasizes collaborative development: multiple users can view and interact with the same dashboards and workspaces, execute cells or components live, and see updates immediately, which streamlines debugging, exploration and stakeholder reviews. The update also focuses on enterprise needs such as access controls, auditability and integrations with data sources and internal systems so organizations can securely surface model outputs to teams. Anthropic positions these features to accelerate productization of model capabilities across data science, analytics and engineering teams, reduce friction between prototyping and production, and enable governed sharing of insights. The article highlights how interactive artifacts aim to make model-driven workflows more transparent and collaborative for businesses adopting Claude-powered tools.

A nuclear engineer proposes using specialized vapor-generating bubbles to boost heat transfer and radically improve cooling for AI data centers, potentially reducing energy demand and easing pressure on power grids. The concept centers on harnessing phase-change and bubble-driven convection inside liquid cooling systems to move heat away from high-power chips far more efficiently than conventional air or liquid cooling alone. The proposal has drawn strong online reaction — including skeptical warnings that it’s not how facilities are usually cooled — and experts note major challenges before deployment: engineering reliable bubble-generation at scale, contamination and maintenance risks, fluid chemistry and materials compatibility, redundancy and failure modes, and regulatory and safety concerns in live data centers. If validated through demonstration and rigorous testing, the approach could help lower operational costs, improve sustainability by enabling waste-heat recovery, and relieve energy-supply strain from rapidly growing AI compute demand, but significant R&D and cautious piloting are required.

Baseten is reportedly raising $1.5 billion just months after completing a previous mega-round, underscoring intense investor interest in AI inference and model deployment infrastructure. According to people familiar with the matter, the new financing would pour fresh capital into Baseten’s platform that helps developers and enterprises deploy, scale, and manage large models in production. The round, if completed, would come at a moment of heavy competition among companies offering inference tooling, low-latency serving, and MLOps features for foundation models and fine-tuned systems. The reported raise is expected to support product development, global expansion, and deeper enterprise integrations as demand for reliable inference and cost-efficient serving grows. TechCrunch’s report cites unnamed sources and notes that Baseten and potential investors did not publicly comment; details such as valuation, lead investors, and specific use of proceeds remain unconfirmed. The potential funding surge reflects broader market interest in AI infrastructure despite mixed macroeconomic signals.

AI-powered toy apps marketed to children are routinely collecting and sharing personal data, raising significant privacy and safety concerns among researchers and child-protection experts. Many apps embed tracking technologies and request or infer sensitive information—such as voice recordings, device identifiers, location data and contact lists—often without clear, age-appropriate disclosures or meaningful parental consent. Investigations show that collected data is frequently transmitted to third-party analytics firms and ad networks, creating risks of profiling and targeted advertising directed at minors. Privacy policies are often vague or buried, and security practices can be weak, increasing the likelihood of unauthorized access or misuse. Regulatory frameworks like COPPA and GDPR are cited as relevant but unevenly enforced, leaving gaps in protections for children using conversational or adaptive AI toys. Experts urge stronger oversight, mandatory transparency about data flows, privacy-by-design defaults, stricter enforcement of existing child-data laws, and improved parental controls. Parents are advised to scrutinize app permissions, favor reputable vendors, and limit data sharing until clearer safeguards are in place.

Snap is spinning off its internal AI video research division into a new independent entity called Dotmo to alleviate operational costs and streamline its strategic focus. The move comes as the social media company balances high infrastructure expenses associated with training advanced generative models against its core advertising and augmented reality business goals. Dotmo will operate as a separate startup, allowing the team to secure alternative venture funding while continuing to develop its proprietary video synthesis technologies. This strategic restructuring reflects a broader trend among major tech companies to offload resource-intensive AI experimentation when those projects do not immediately align with near-term revenue generation or core product roadmaps.

Illinois is advancing a ban on wearing smart glasses while driving to curb potential driver distraction and safety risks. The piece outlines recent legislative efforts aimed at restricting in-car use of augmented-reality eyewear and similar head-mounted displays, citing concerns from lawmakers and safety advocates that visual overlays, notifications, or live-streaming could divert attention from the road. It notes enforcement and definitional challenges—how to draw lines between benign heads-up displays, accessibility tools, and distracting AR features—and mentions industry reactions and privacy implications tied to camera-equipped devices. The author expresses conditional agreement with the ban, arguing public-safety priorities justify cautious regulation while warning against overly broad rules that might stifle useful, hands-free navigation and accessibility applications. The article calls for targeted, technology-neutral laws, clearer technical definitions, and collaboration between regulators, safety experts, and manufacturers to balance innovation with road safety.

OpenAI is assembling a high-profile executive and advisory team to accelerate preparations for a prospective IPO, signaling serious intent to enter the public markets. The company is recruiting senior finance, legal, compliance and capital-markets professionals — including experienced CFO-level and investment-banking talent, regulatory and governance experts, and seasoned investor-relations advisers — to bolster its readiness for the scrutiny and reporting demands of a public listing. These hires are intended to strengthen corporate governance, financial controls and external communications, help set valuation expectations, and navigate regulatory and investor questions about safety, revenue models and partnerships. The move reflects pressure to professionalize operations amid rapid product commercialization and fierce competition in AI. Bringing on established public-markets veterans should increase credibility with IPO investors and regulators, potentially shaping timing, pricing and post-IPO strategy while highlighting how leading AI firms are preparing to balance innovation with public-market responsibilities.

Amazon is moving to challenge Nvidia’s dominance by offering its in-house AI accelerators for sale beyond AWS, aiming to make Trainium and Inferentia chips available to third parties and on-premises customers. The company claims the chips deliver competitive price-performance and energy efficiency for large transformer workloads, and plans to pair hardware availability with its Neuron software stack and integrations for PyTorch and TensorFlow to ease migration. The piece outlines Amazon’s strategy to expand from cloud-only deployments to a broader hardware business, underlining potential market pressure on Nvidia’s margins and cloud pricing. Analysts note Amazon still faces hurdles: a smaller developer ecosystem compared with Nvidia’s CUDA, workload compatibility, and the need to prove performance on diverse model families. The article concludes that wider distribution of Amazon’s accelerators could lower costs and broaden choices for enterprises, but Nvidia’s entrenched ecosystem and software advantages remain significant obstacles.

Nearly half of U.S. singles report negative views of AI in dating, according to a survey cited by Match. Respondents expressed worries that AI can undermine authenticity and trust on dating platforms, enabling deceptive profiles, misleading imagery and inauthentic messaging. Privacy and data-use concerns also contribute to the skepticism, with many singles uneasy about machines shaping romantic interactions. Despite widespread reservations, some users acknowledge potential upsides such as improved matching, time-saving conveniences and safety features if AI is used responsibly. Match highlights the need for transparency, clear disclosures and stronger safeguards to preserve human agency and trust. The findings suggest dating apps may face pressure to implement verification, labels for AI-generated content and tighter privacy controls as they balance innovation with user comfort and safety. These mixed attitudes point to an industry turning cautious about deploying AI features without visible guardrails.

Karamo Brown has launched Kē, a wellness app that uses an AI digital clone of the Queer Eye life coach to deliver personalized coaching, guided practices and reflective exercises. The app centers on conversational interactions with a synthetic Karamo persona trained to emulate his coaching style and voice, offering goal-setting, journaling prompts, mindfulness and motivation tailored to users’ needs. Brown presents the AI clone as a scalable way to extend his approach to wellbeing beyond one-on-one sessions. Kē is positioned as a supplement to—not a replacement for—human care and includes product safeguards and user controls intended to address consent, privacy and misuse concerns. The launch reflects a growing trend of celebrity-backed AI companions in wellness and raises questions about ethics, data use and therapeutic boundaries as more apps combine personal-brand authenticity with generative AI capabilities.

Google Cloud argues that agentic AI—autonomous, goal-directed AI agents—will become central to enterprise automation and productivity, shifting work from manual orchestration to agent-led task execution. The piece highlights Google’s strategy of combining large language models with an agent framework, orchestration tools, and deep connectors into enterprise data and systems to enable end-to-end workflows that can plan, act and adapt. The article details how Google is packaging capabilities (model hosting, developer tooling, security and governance, observability and MLOps) to make agentic solutions practical for businesses, while calling out use cases such as automated customer support, IT and DevOps tasking, knowledge worker augmentation and process automation. It also discusses common implementation challenges—data access, trust, safety, auditability and integration with legacy systems—and positions Google’s cloud infrastructure and AI offerings as competitive differentiators in a market where enterprises seek both powerful models and enterprise-grade guardrails.

Google has officially retired the Nest Audio and Nest Mini product lines, meaning the company has stopped selling those models and is winding down long-term support. Existing devices will continue to work for basic functions in the near term, but owners should expect fewer feature updates, eventual end of security patches, and a declining window for warranty or repair options as Google focuses development on newer hardware. Current owners are advised to review their Google Home settings, back up important routines and automations, and consider migrating critical smart-home integrations to newer Google devices or compatible alternatives. Some services or advanced Assistant features may be limited over time, and users who rely on up-to-date security or the latest Assistant capabilities should plan for replacement. The change reflects Google’s product consolidation and ongoing platform shifts; while day-to-day voice control will likely remain for now, the long-term user experience will increasingly favor recent Nest hardware and software ecosystems.

ChatGPT can recreate a vivid 1990s action-figure persona and even recall forgotten personal details, producing a richly detailed toy concept that felt uncannily familiar. The author guided the model through designing an action figure inspired by 1990s aesthetics — sculpted hair, exaggerated muscles, removable gear, signature accessories, a catchy codename and backstory — and ChatGPT produced textured descriptions, packaging copy and suggested play features that evoked real childhood toys. Beyond creative writing, the interaction highlighted the model’s ability to maintain conversational context and surface memories or small facts the author had not mentioned recently, prompting moments of genuine nostalgia. The piece reflects on the strengths (rapid ideation, personalization, playful worldbuilding) and risks (overconfidence, invented specifics, privacy concerns about memory-like behavior) of using generative AI for personal creative projects, suggesting both delightful applications and the need for caution around accuracy and data retention.

Stellantis, Wayve, and Uber have announced a strategic partnership to develop and deploy advanced autonomous driving technologies for a global robotaxi fleet. This collaboration aims to leverage Stellantis’s automotive engineering and manufacturing expertise, Wayve’s cutting-edge end-to-end AI software, and Uber’s extensive global ride-hailing network to overcome the scalability challenges currently facing automated transportation. The venture focuses on moving beyond traditional map-based autonomous systems by utilizing Wayve’s generative AI and embodied intelligence. By integrating these systems into Stellantis vehicles available specifically for the Uber platform, the companies intend to offer safer, more efficient, and widely accessible autonomous mobility solutions across various international markets.

Deploying AI agents requires moving quickly but with strict safeguards to avoid operational, legal, and reputational harms. Start by running targeted pilots and sandbox experiments that limit scope, data exposure, and user impact; use staged rollouts and canary releases so problems surface early and are contained. Complement rapid experimentation with robust technical guardrails: access controls, rate limits, input/output filters, observability, detailed logging, and automated monitoring and alerting tied to measurable safety and accuracy metrics. Institutionalize governance and cross-functional oversight so product, security, legal, and compliance teams assess risk, vendor SLAs, data provenance, and contractual protections before broad deployment. Maintain human-in-the-loop processes and clear fallback procedures for ambiguous or high-stakes decisions, plus incident response and rollback plans. Invest in user training, explainability, and continuous feedback loops to refine agents over time. Overall, the piece urges organizations to combine aggressive iteration with conservative controls to capture AI benefits while minimizing harm.

The current generation of Silicon Valley wealth is undergoing a fundamental shift driven by the rapid rise of artificial intelligence and significant IPO activity. Financial advisers catering to the tech elite note that young tech millionaires are increasingly prioritizing liquidity and defensive asset strategies over the traditional long-term holding patterns seen in previous decades. This new era of wealth is characterized by shorter timelines for liquidity, fueled by the aggressive deployment of capital into AI startups. As founders realize quicker exits or valuations, they are shifting their focus toward complex tax planning and philanthropy, seeking to protect rapidly accrued gains amid high-profile market volatility.

Adobe is expanding AI-powered assistants across Creative Cloud in a public beta to guide users through creative workflows, automate repetitive tasks, and help produce assets faster while keeping human oversight. The new assistants—built on Adobe’s Firefly generative models and integrated into apps like Photoshop, Illustrator, Premiere Pro, Lightroom, and Express—aim to offer step-by-step guidance, prebuilt workflows, and contextual suggestions that steer users toward predictable, high-quality outcomes rather than replacing creators. Adobe emphasizes human-in-the-loop controls, provenance and safety features (such as content credentials and usage controls), and options to edit or reverse AI-generated results. The company positions these agents as productivity tools for professionals and casual creators alike, addressing common tasks (layout, masking, color grading, object generation) and promising interoperability across Creative Cloud. The rollout raises familiar concerns about copyright, job impacts, and model limitations, which Adobe says it is addressing through transparency, safeguards, and a staged public beta.