Schneider Electric and Foxconn have entered a strategic partnership to accelerate the development of next-generation data centers, focusing specifically on supporting the high-power demands of artificial intelligence and high-performance computing. The collaboration leverages Schneider’s energy management expertise and Foxconn’s manufacturing scale to optimize power efficiency and cooling infrastructure in mission-critical facilities. By integrating advanced AI-ready infrastructure, the companies aim to address the energy challenges posed by generative AI workloads. This partnership will prioritize sustainable and scalable solutions, ensuring that data centers can handle the increased thermal loads and density requirements synonymous with modern AI technologies.

A federal judge has allowed adult site Blacked.com to pursue a lawsuit against Meta, concluding the company can be sued over allegations it scraped Blacked.com’s pornographic content without permission. The ruling denies Meta’s attempt to dismiss the complaint and permits Blacked.com’s core claims to move forward to litigation. Blacked.com alleges Meta collected and copied images and videos from its site, then used or displayed that content across Meta’s services in ways that harmed the plaintiff’s business and rights. The complaint asserts causes of action based on unauthorized copying and related claims; the court found the factual allegations sufficient to survive an early dismissal motion and warrant further discovery. The decision underscores legal risks for platforms that harvest or index third-party content and may motivate publishers to push for stronger enforcement or contractual protections. It could influence how courts treat large tech companies’ content-scraping practices and the scope of liability for aggregating or republishing online material.

Anthropic has retracted its experimental "Fable" and "Mythos" AI models following concerns raised by federal authorities regarding their potential for misuse. The decision came after US officials identified that these specific models could be susceptible to "jailbreaking," a process where users bypass built-in safety guardrails to generate harmful or restricted content. This incident highlights the escalating scrutiny surrounding the security of generative AI systems. By prioritizing safety and regulatory compliance over continued deployment, Anthropic aims to mitigate risks associated with unchecked model capabilities. The company is now working to evaluate its internal security protocols to ensure future models withstand rigorous federal standards before public availability.

Salesforce is acquiring AI customer service platform Fin for $3.6 billion to accelerate its AI-driven customer service offerings and deepen Service Cloud’s generative AI capabilities. The deal brings Fin’s conversational AI, knowledge retrieval and automation tools into Salesforce’s product suite, aiming to improve automated support, shorten resolution times and enable more personalized, context-aware customer interactions. Salesforce expects the acquisition to strengthen its competitive position in enterprise service automation and to speed product development by integrating Fin’s models, tooling and customer deployments. The move reflects broader industry consolidation as major cloud and software vendors invest in specialized AI startups to deliver more capable, integrated customer service solutions. The companies anticipate continuing support for existing Fin customers while working to fold capabilities into Salesforce’s platforms and partner ecosystem.

AI can streamline and simplify Linux system management by automating routine tasks, generating and debugging commands and configurations, and surfacing insights from logs and metrics. Key practical uses include: 1) automating repetitive administration via generated shell scripts, systemd units, and cron jobs; 2) producing, optimizing and explaining shell commands and scripts to accomplish specific tasks; 3) creating and templating configuration files for services (nginx, systemd, sshd, etc.); 4) analyzing logs and monitoring output to detect anomalies and recommend remediation; 5) assisting with package management, container orchestration and deployment snippets; 6) performing basic security auditing, hardening suggestions and incident-response guidance; and 7) generating documentation, runbooks and onboarding materials to speed operator training. Practical advice in the article emphasizes verifying AI outputs, avoiding direct exposure of secrets, using local or trusted models where necessary, and integrating AI into existing workflows (APIs, CI/CD, monitoring tools) while maintaining human review for critical changes.

UK business leaders can make AI practical and valuable by focusing on clear business problems, data readiness, governance and targeted skills development. The article argues that successful AI adoption starts with leadership aligning on specific use cases that deliver measurable ROI rather than pursuing technology for its own sake. It highlights common barriers faced by UK organisations — poor data quality, skills shortages, unclear governance, legacy systems and concerns about cost, security and regulation — and recommends pragmatic steps to overcome them. Recommended actions include starting with small, well-scoped pilots; building cross-functional teams that combine domain experts, data scientists and IT; investing in data governance and MLOps to ensure repeatability; and partnering with vendors or consultants where internal expertise is lacking. The piece also stresses the importance of executive buy-in, ethical frameworks and compliance with evolving UK rules so projects scale reliably from pilot to production while delivering business value.

Sarvam AI has achieved unicorn status following a successful $234 million Series B funding round spearheaded by HCLTech. The Bengaluru-based startup specializes in developing large language models customized for Indian languages and deep-tech applications, aiming to bridge the accessibility gap in AI technology across diverse linguistic demographics. This latest capital infusion arrives amid a broader surge in demand for sovereign AI solutions within the Indian market. Sarvam plans to utilize the funds to scale its compute infrastructure, bolster its research and development team, and expand its enterprise-focused generative AI product suite, further solidifying its position in the rapidly evolving Asian AI landscape.

FBI has seized and disrupted Outsider Enterprise, an AI-powered mass phishing service that was generating over a million phishing URLs to harvest credit card information and account passwords. The service used automated tools and AI-generated content to craft convincing phishing pages and messages at scale, enabling operators to deploy vast numbers of fraudulent URLs and clone legitimate sites to trick victims into submitting sensitive data. Law enforcement action targeted the service’s infrastructure and web domains to shut down its operations and limit further fraud. The takedown highlights how AI is being abused to automate and scale cybercrime, increasing the speed and realism of social engineering campaigns. The disruption demonstrates growing efforts by authorities to combat AI-enhanced phishing, but underlines that defenders must strengthen detection, domain monitoring, user education, and authentication practices to mitigate rapidly evolving automated threats.

Steam's latest hardware survey indicates that AMD's Radeon RX 9070 XT has a larger presence in the PC gaming market than many expected, showing measurable uptake among Steam users. The survey data suggests the 9070 XT is carving out a foothold thanks to competitive performance at 1440p, improved availability compared with some rivals, and pricing that appeals to gamers seeking high frame rates without stepping up to the most expensive flagship cards. The article cautions that Steam's hardware survey is a limited, self-selecting sample and not a definitive market-share measurement, so the findings should be interpreted with care. It compares the 9070 XT's Steam share to other recent GPUs, notes factors that could influence adoption such as drivers, game optimization, and supply, and suggests that while the card isn't ubiquitous, its real-world presence is notable and could signal healthy demand for AMD's current generation among mainstream and enthusiast gamers.

Gemini on Google TVs now lets you use natural-language voice commands to change picture settings instantly or describe what's wrong and have the assistant attempt a fix. Users can say commands like "set picture mode to Sport" or simply explain issues (too dark, colors off, motion judder) and Gemini will map that input to the TV's picture controls, adjusting brightness, contrast, color, motion smoothing and related parameters on compatible Google TV devices. The upgrade is designed to make picture adjustment accessible to non-technical users, saving time compared with manual menus and offering quick on-the-fly tweaks. Google integrates Gemini's conversational capabilities into the TV experience so it can interpret descriptive feedback and recommend or apply changes. Availability appears limited to select models and regions with a staged rollout, and results may vary—hardware limits and calibration needs mean it won't replace professional tuning, but it simplifies everyday picture fixes for most viewers.

Orbital technology is experiencing a paradigm shift as startups integrate edge computing and machine learning to enable satellites to perform autonomous image analysis in space. By processing data directly on the satellite rather than transmitting raw imagery to ground stations, these systems can identify specific features—such as vessels, clouds, or deforestation—in near real-time. This development reduces the massive data bottlenecks currently plaguing satellite networks and allows for faster actionable intelligence. The transition from passive image collection to active, AI-driven edge processing turns static satellites into intelligent sensors capable of immediate event detection. This capability is expected to revolutionize sectors like maritime surveillance, environmental monitoring, and disaster response by providing rapid insights without human intervention or delayed terrestrial processing cycles.

Modern AI data centers must transition from being perceived as traditional compute-heavy systems to integrated energy-managed infrastructures. As AI workloads scale, the physical constraints of power delivery and thermal management have become the primary bottlenecks, often overshadowing raw processing capacity. Energy density is now the defining architectural challenge, requiring facility designers to prioritize utility infrastructure and cooling efficiency over conventional server density. Success in the AI era demands a paradigm shift where power infrastructure and liquid cooling integration are considered the foundational components of the compute fabric, rather than ancillary utility services.

Consumers are increasingly embracing AI-powered shopping agents, with a significant majority expressing readiness to utilize these tools for personalized purchasing assistance. Accenture’s latest research highlights that over 60% of consumers feel comfortable delegating routine shopping tasks to AI if it leads to time savings and better tailored offers. Despite this growing openness, the report emphasizes that trust hinges on transparency and data security. Shoppers remain cautious about how their personal information is processed, often requiring clear proof of value before fully adopting automated assistants. Businesses must bridge this gap by prioritizing ethical AI deployment to turn consumer curiosity into sustained long-term loyalty.

AI pilots often stall because organizations treat them as isolated technical experiments rather than end-to-end business initiatives that require data, process and change management. Common causes include vague or misaligned objectives, poor-quality or inaccessible data, lack of production-ready engineering (MLOps), insufficient executive sponsorship, unclear metrics for success, and underinvestment in integration and operationalization. These problems make pilots look promising in demo environments but fail when exposed to real-world complexity, compliance needs and scale. To scale successfully, organizations must start with well-defined customer or operational outcomes, invest in data foundations and governance, and build cross-functional product teams that combine business, data science and engineering. Establishing MLOps, clear success metrics, change management and executive accountability accelerates deployment. Continuous monitoring, ethical and risk frameworks, and upskilling or hiring for production engineering and data roles turn pilots into repeatable, measurable value streams rather than one-off proofs of concept.

Most employees are using unauthorized AI tools at work, creating a significant enterprise liability, according to a new study. The research reveals widespread, often unsanctioned use of generative AI and consumer-grade models (such as public chatbots and third-party apps) to speed tasks, draft content, and analyze data—frequently without IT approval or data protection controls. That shadow-AI usage raises major risks including sensitive data exposure, intellectual property leakage, regulatory noncompliance, and increased attack surface for threat actors. Organizations are reportedly ill-prepared to detect or govern these activities, prompting calls for clearer policies, stronger data-loss prevention, and enterprise-grade AI platforms. The study recommends a combination of employee education, sanctioned AI services, robust access controls, monitoring and auditing, and cross-functional governance led by security and legal teams to balance productivity gains with risk management. Vendors and security teams are urged to prioritize visibility and controls to mitigate the emerging liability from shadow AI.

XPENG is doubling down on artificial intelligence to achieve full autonomy, focusing on shifting from rule-based software to end-to-end neural network models. By leveraging deep learning, the company aims to move away from manually coded driving logic, allowing its vehicles to learn and make decisions more like human drivers in complex, real-world traffic scenarios. During the interview, XPENG's leadership emphasized that massive data collection and high-performance computing are the foundations of this transition. This strategy aligns with the broader industry trend of utilizing AI to manage edge cases in autonomous driving, positioning the company to compete globally as it scales its advanced driver-assistance systems and future robotaxi ambitions.

A rapid surge of layoffs across AI startups and major tech firms has created a volatile environment that risks undermining the sector's long-term health and public trust. Companies that aggressively expanded teams during the AI hiring boom are now cutting headcount to control costs as revenue growth and product monetization lag; high inference and infrastructure expenses, overscaled research orgs, shifting investor sentiment and an uncertain macro environment are driving the retrenchment. The wave is concentrated in both early-stage startups and established players, leaving large cohorts of specialized engineers, researchers and operations staff suddenly displaced. The fallout could accelerate consolidation, slow product development and hollow out expertise—especially in safety, data, and deployment teams—while increasing competition for fewer senior hires. Workers face a difficult market, and governments and firms may need to invest in reskilling, clearer labor protections and better transition support. The piece calls for more disciplined hiring, transparent communications, and strategic prioritization to prevent short-term cost cutting from inflicting long-term damage on AI capability and trust.

Orbio raised $21 million to accelerate deployment of automated, AI-enabled hiring and onboarding tools for frontline workforces, aiming to reduce time-to-hire and improve new-hire retention. The company’s platform combines automated candidate sourcing and screening, conversational onboarding flows, compliance documentation handling, scheduling and first-day training modules to streamline processes that traditionally burden hourly and frontline managers. Orbio emphasizes mobile-first experiences, multilingual support and integrations with applicant tracking systems, HRIS and payroll providers to create end-to-end workflows that move candidates from application to productive work faster. Proceeds from the round will fund product development, expansion of sales and customer-success teams, and scaling operations into new markets and verticals such as retail, hospitality and logistics. The raise reflects growing market demand for automation in workforce management and competition among startups building AI-driven hiring and onboarding solutions for hourly workforces.

The author indexed 669 GB of GoPro footage on an M1 Max by running local machine-learning models to produce searchable transcripts and visual embeddings, enabling fast offline search and organization. The workflow extracts audio for transcription, generates per-frame or per-segment visual features, and stores metadata so clips can be found by spoken words, visual similarity, or timestamps. Running models locally preserved privacy and avoided cloud costs while delivering responsive query times on a single laptop. Practical details emphasize trade-offs: pre-processing and embedding require significant compute and storage but are one-time costs; indexing choices (segment length, feature type, indexing backend) affect recall and speed. The write-up highlights tooling decisions, speed and resource observations specific to an M1 Max, and use cases like personal archives, travel footage management, and small-scale media libraries where local ML indexing is a viable alternative to cloud services.

Many planned AI data centers in the US are concentrated in drought-hit regions, raising urgent questions about local water availability and electrical capacity. Companies are pursuing sites in the Southwest and other arid areas because of land, tax incentives and proximity to fiber, but those locations face constrained freshwater supplies and strained grids. These facilities can require large amounts of water for evaporative cooling and backup systems, and their huge electricity demand risks increasing reliance on fossil-fuel generation or stressing transmission infrastructure. Local communities and environmental advocates warn of competing demands between data centers, agriculture and residents, while utilities and regulators confront permitting, water-rights and grid-stability challenges. Operators are exploring mitigations — closed-loop cooling, air and immersion cooling, recycled or treated wastewater, on-site renewables and battery storage — but trade-offs remain between water use, energy consumption and cost. The siting decisions highlight tensions between economic development incentives and sustainable resource management for AI-scale infrastructure.