NVIDIA Launches AI Data Factory To Scale Robot Training 

News Summary 

  • The blueprint enables processing and organizing large amounts of data, generating computer-created (synthetic) data, applying Decision-making algorithms (reinforcement learning), and testing physical artificial intelligence (AI) models for computer vision-based agents, robotics systems, and self-driving vehicles.  
  • Cloud providers use the blueprint to turn large-scale computing power into agent-driven data production tools.  
  • Top physical AI developers are using Bluetooth to speed up work on robotics, vision AI agents, and self-driving vehicles.  

At GTC, Nvidia announced the Nvidia Physical AI Data Factory Blueprint — an open reference design that automates the generation (creation), refinement (improvement), and validation (quality and accuracy checks) of training data, making it cheaper, faster, and simpler to train physical AI systems at scale.  

With the blueprint developers can use, NVIDIA, Cosmos, Open World Base models, and top coding agents, they can turn small training datasets into large, varied ones. This includes rare and unusual cases that are hard or costly to collect in real life.  

NVIDIA is partnering with Microsoft Azure and Nebius to bring the open blueprint to their cloud platforms. This enables developers to leverage advanced computing to generate large training datasets. Companies such as Field AI, Hexagon, Robotics, Linker, Void Vision, Milestone Systems, RoboForce, SkildAI, Teradyne Robotics, and Uber are already adapting Blueprint to accelerate robotics, vision, air, and autonomous vehicle development.  

Physical AI is the next frontier of the AI revolution, where success depends on the ability to generate massive amounts of data, said Rev Lebaredian, Vice President of Omniverse and Simulation Technologies at Nvidia. Together with cloud leaders, we are providing a new kind of agentic engine that transforms compute into high-quality data, enabling the next generation of self-governing systems and robots to come to life. In this new era, Compute is data.  

A unified engine for physical AI improves with data, computing power, and larger models. The physical AI data factory blueprint provides a single reference design for converting raw data into model-ready training sets through modular, automated workflows.  

  • Curate and search: Nvidia Cosmos Curator is a tool that handles, improves, and labels large datasets that include both real-world and synthetic (artificially generated) data.  
  • Augment and multiply: Cosmos Transfer greatly increases and diversifies the selected dataset, combining real-world and computer-generated data to better represent rare and unusual scenarios across a range of environments and lighting conditions.  
  • Evaluate and validate column Nvidia Cosmos Evaluator (which uses Cosmos Reason) and Evaluation Algorithm, and is now on GitHub. It automatically checks scores and filters data to ensure accuracy and readiness for training.  

Nvidia is using the physical AI data factory blueprint to train and test Nvidia Alpamayo, the first open resource-based vision–language–action model for rare autonomous driving situations. Skild AI and Uber are also using the blueprint to add advanced robot models and self-driving vehicles.  

Agent Driven Orchestration At Scale 

Many robotics developers lack the resources to set up and manage the complex AI systems needed to generate data at scale.  

NVIDIA OSMO is an open-source tool that unifies Windows across diverse computing environments. By reducing manual tasks, developers can focus on building models. Osmo integrates with coding agents such as Claude Code. OpenAI codecs and cursor-enabled AI agents manage resources, resolve bottlenecks, and accelerate model deployment at scale.  

Powering the Global Physical AI Ecosystem  

Cloud service providers are key to offering the first AI infrastructure, machine learning tools, and orchestration services that developers need. To build and launch physical air at scale  

Microsoft Azure is integrating the physical AI data factory blueprint-a set of guidelines and reference architectures for building, training, and validating artificial intelligence systems that interact with the physical world – into an open physical AI. Tool chain now available on GitHub. The Blueprint offers integration with Azure services such as Azure IoT operations (a platform for managing and analyzing Internet of Things data), Microsoft Fabric (AUD Data and Analytics Platform), real-time intelligence (a service for processing streaming data) and Microsoft Foundry (a suite of development tools) to enable enterprise-bred agent-driven workflows for quickly and at-scale training and validating physical AI systems.  

Early adopters use the Azure Physical AI toolchain to accelerate data generation, refinement, and testing for perception, mobility, and reinforcement learning projects.  

Nebius has added Osmo to its AI cloud, enabling developers to use the blueprint to set up data pipelines ready for production and customized to their needs. Nebius’s system supports the whole physical AI stack, combining Nvidia RTX Pro 6000 Blackwell Server Edition GPUs with fast storage, built-in data management and labeling, serverless execution, and managed inference.  

Early users like Milestone Systems, Voxel51, and RoboForce – members of the previously mentioned group – are using the blueprint on Nebius infrastructure to speed up model development for video analytics, AI agents, self-driving vehicles, and Steel humanoid robots.  

The Nvidia Physical AI Data Factory Blueprint should be available on GitHub in April.  

You can watch the GTC keynote from NVIDIA founder and CEO Jensen Huang and check out the sessions.  

Source: NVIDIA Announces Open Physical AI Data Factory Blueprint to Accelerate Robotics, Vision AI Agents and Autonomous Vehicle Development

AI-Driven Layoffs Surge as US Firms Replace Key Roles

The latest U.S. Securities and Exchange Commission (SEC) filings suggest a growing trend of companies laying off employees. Organisations have begun automating some functions once performed by human employees; for example, companies are now using AI to assist with complex decision-making, data analysis, and other operational tasks. In this way, AI has fundamentally changed how employers and employees interact, affecting millions of employees and prompting numerous organisations to reconsider how best to manage their workforces and boost productivity.  

AI Integration and Workforce Transformation  

Businesses across sectors are using AI systems to replace workers or augment employee roles, with applications including customer support, accounting, supply chain, and analytics. Customers will benefit from improved cost efficiency and higher productivity as these functions are all becoming automated.    

Small, medium, and large organisations are using the speed at which AI can analyse massive amounts of data, uncover patterns, and execute many tasks with little or no error as their main reasons for moving to automated systems. In addition to the large savings that come from automating processes, there are also questions about what will happen to future jobs, how workers will keep their skills up to date, and finally, what will those individuals do once they are out of work if they are moved out of their current job into other opportunities?  

Industries Most Affected  

The technology industry is known for and has a long history of using data to make important decisions. Artificial intelligence and all that is associated with it, such as machine learning, are now being utilised by businesses in the technology industry for many tasks, such as coding, testing, and troubleshooting, that were traditionally done manually. The financial industry has developed machine learning systems that can perform many functions, including studying portfolios for investment opportunities and detecting fraud.  

Manufacturing and retail are now being impacted by robotics, predictive technology, and automated inventory systems. Creative industries such as marketing and media have been using AI to create content in place of human writers. This indicates an increase in the automation of work beyond routine processes into areas that require higher levels of cognitive function. 

Strategic Deployment of AI  

Companies are implementing AI to create more effective, efficient workflows, thereby improving both productivity and quality. By reassigning human employees to perform tasks of higher value while automating tasks that are repetitive and typically have an associated potential for error, i.e., duplication of effort, companies believe that they will be able to improve overall productivity by remaining innovative and providing high-quality services.  

When companies implement AI systems, they often also offer reskilling initiatives, i.e., programmes that help current employees learn to work with or alongside an AI system or focus on jobs that require uniquely human abilities like leadership, creativity, and emotional intelligence. However, there are many variations between individual companies and industries regarding the extent of reskilling programmes provided to former employees, which is, invariably, not available to all displaced persons as a source of assistance in finding new employment.  

Economic Implications  

The increase in AI employment terminations will have a major effect on the economy. For example, when companies cut payroll expenses through technology, it can improve their profit margins. That said, if many people who were displaced from their jobs become consumers, it could affect their spending. This could also contribute to social inequality since consumers will have reduced disposable income. Economists also state that failure to take proactive measures in the workforce could worsen the income gap and put additional pressure on social safety nets as automation increases.  

Investors are watching these events closely because they will affect how companies operate, how investors value them, and how competitive they are with one another. Companies that can efficiently use automation and effectively manage their workforces will likely achieve significant cost savings and a significant increase in profitability.  

Ethical and Regulatory Considerations  

SEC filings illustrate the growing importance of transparency and accountability as companies introduce workforce changes driven by artificial intelligence. Investors and regulators are showing interest in how companies report the extent and impact of AI on worker employment, as well as any risks to their ongoing business operations and public image.  

Witnessing the introduction of AI raises ethical questions such as whether companies should have a duty to provide for displaced employees displaced by technological advancement, whether companies should conduct their employment relationships consistently with fair labour practices, and whether they can continue to earn the trust of their stakeholders after they have transformed their organisations. It’s a significant challenge for corporate leadership now and in the future.  

Worker Perspectives  

AI-based layoffs create an unprecedented shift in employees’ relationship to job stability and their future planning at work. For example, when workers’ functions are automated, they will lose their jobs, and those remaining will need to modify their workflows and coexist within an environment filled with intelligent systems.  

The effects of this transition highlight the need for ongoing improvement in learning and skill development, particularly in technological literacy, data analysis, and solution development. Companies that invest in upskilling their workforce will be better positioned to mitigate some of the negative consequences of automation and retain talent essential to long-term success.  

AI’s Role in Corporate Strategy  

AI enables companies to make decisions guided by objective data rather than subjective opinions or speculation. With this capability, an organisation can identify emerging market trends, predict consumer demand for products/services, and optimise resource allocation more quickly than with only human teams.  

The use of AI gives corporations a competitive advantage, further validating AI as an integral part of their growth strategies through workforce restructuring. Corporations are beginning to view AI not only as a tool but also as a key force behind their future competitiveness in fast-changing markets.  

Balancing Innovation and Responsibility  

AI usage is on the rise, and with this increase comes the challenge of how companies can create new technologies while being ethically responsible. To maintain public confidence and achieve sustainable success, companies need to communicate transparently, support those who lose their jobs to automation, and carefully manage workforce transitions.  

Emerging AI governance frameworks provide companies with an effective roadmap for deploying automation responsibly, boosting productivity, and fostering innovation.  

Future Outlook  

There is also a belief that the trend toward automation as a driver of workforces will continue and that automation will be expanded further into additional roles and/or industries. Because of these factors, organisations will continually be required to evolve through investments in AI capabilities while also strategically managing their human capital.  

Employers anticipate that as AI evolves into a more sophisticated form, it will be able to perform advanced cognitive tasks, leading to changes in how people are employed. This shift cannot be achieved without the coordination of businesses, governments, and educational institutions to provide a workforce that can respond to and adapt to technological change.  

Conclusion: A Transformative Shift  

SEC filings indicate that job cuts due to artificial intelligence are not just happening at select companies; it is indicative of a larger shift occurring throughout the American economy. The introduction of automation across sectors is changing the way people work, the nature of jobs, and the traditional workforce structure.  

Companies that have embraced AI as part of their operations, while emphasising employee support and retraining through upskilling programmes, may do well as the labour market continues to evolve; those that don’t embrace this approach face the risk of operational difficulties and reputational damage. This new phase in the history of work represents a turning point in how work is defined, as well as in the need for innovative, flexible, and responsible approaches to deploying these technologies.

Source: https://www.sec.gov/ 

AI Model Prices Surge as Demand for Reasoning Power Rises

Newly submitted filings to the U.S. Securities & Exchange Commission have shown a large increase in costs associated with the use of advanced AI models, as businesses continue to rely on AI for increasingly complex reasoning, decision-making, and analytical needs across many business functions. Organizations are investing heavily in AI infrastructure across industries, including finance, technology, health care, and logistics, thereby enabling businesses to accommodate increasing demand for computational power driven by this surging reliance on AI technology. The increase in prices refers to both the increasing complexity of the technical design of AI systems and the growing importance of these technologies within the corporation for corporate strategy, operations, and innovation.  

Rising Costs Driven by Complexity  

The need for artificial intelligence systems that reason and solve problems has soared dramatically in recent years. Whereas past AI models primarily performed pattern recognition and automated tasks, the current generation can analyze large amounts of data, provide predictive insights, and help businesses make strategic decisions. To provide these higher-order services, the advanced capabilities of current-generation AI models require extensive computing power, specialized hardware (e.g., high-performance graphics processing units), and considerable energy, all of which combine to drive up the total cost of ownership.  

Organizations that implement AI models are finding that their operational costs from operating AI systems, including licensing fees, cloud computing expenses, equipment upgrades, and ongoing maintenance, have increased significantly. Companies are struggling to balance their investments in artificial intelligence with the anticipated productivity, efficiency, and competitive advantages it will deliver to their businesses.  

Sectors Experiencing the Sharpest Impact  

Industries such as Technology and finance are affected by the escalating costs of AI development and implementation. This is due in large part to the fact that these two industries require extensive real-time data analysis, predictive modeling, and automated workflows. i.e., financial organizations use AI to assess risk, detect potential fraud, and manage/optimize their investment portfolios. Similarly, technology organizations are able to leverage advanced logical reasoning systems/common software packages to create/develop computer programs, drive efficiencies within the organizations themselves, and also potentially add value to their current product or service offerings by continuing to develop new software applications on top of/utilizing existing ones that they have already developed. 

Similarly, AI is significantly impacting the healthcare and logistics sectors by significantly increasing expenditure within these industries. Healthcare organizations use large-scale AI systems for everything from diagnosing patients to developing therapeutic treatment plans and evaluating supply chain processes, all of which require substantial computational resources for continued operation, thereby increasing operational budgets.  

Balancing Performance and Cost  

Many companies are looking for methods to control expenses while getting the highest performance from their AIs. This can include optimizing the processing capacity of the models being used by developing new ones; using on-device processing rather than sending the process to the cloud during peak hours; and using the cloud only when necessary due to limited cloud resources. Businesses are also reviewing how to balance model size, speed, and reasoning capabilities to get the most out of their investment in high-performance AI, i.e., to create future-proof products.  

The other companies in the space say that developing high-performance AI products is becoming more expensive, and they may have to keep spending on high-performance AI just to stay competitive. This will force these firms to continue, and possibly increase, their spending on high-performance AI as the cost of doing business (e.g., labor) rises, and additional competitive pressures keep them in business.  

Hardware and Infrastructure Requirements  

The rise in expense associated with AI is primarily the result of having to invest money to construct the systems necessary to operate these intricate and extensive AI models. High-performance graphics processing units (GPUs) and other processors designed specifically to handle AI, as well as data storage facilities to manage massive amounts of data, are key components of that infrastructure. High-performance equipment will be critical for running the numerous multi-step AI models used to perform complex tasks. 

Therefore, businesses that build and maintain AI infrastructure will also require significant electricity to power it, thereby increasing overall operational costs for many companies that want to implement AI. Companies that do not allocate sufficient investment in hardware to maximize the use of their AI applications will either not use their AI to benefit them in a timely manner or process data more slowly than their competitors in dynamic industries.  

Market Implications  

As AI costs rise, market structure is changing: larger companies with more resources have an advantage over smaller firms and can better deploy cutting-edge AI models. This may lead to consolidation across industries due to the large accretive investments required by companies to gain an AI advantage.  

Therefore, AI investment is of key interest to investors because the rising cost of advanced models will impact a company’s future profitability, operating margins, and long-term growth strategies; those firms that effectively manage their AI investments and achieve results will likely earn a competitive advantage.  

Adoption Strategies and Optimization  

Organizations reduce rising operational costs by pruning models, optimizing parameters, and using hybrid computing techniques that combine cloud and on-device processing for their existing models. The above actions reduce the organization’s computational and energy needs while still allowing it to maintain the model’s capacity to reason. 

Companies are also exploring shared AI services and subscription-based access to reduce upfront costs while still enabling access to powerful reasoning models for business-critical applications. Companies with limited budget resources can now access advanced AI technology through these methods, making it more affordable.  

Ethical and Operational Considerations  

When developing artificial intelligence, companies must take into consideration how they can ethically use it as well as three main areas to evaluate: 1) fair use; 2) open decision-making procedures; and 3) being accountable to the community for their actions. Ensuring that businesses’ AI makes fair, unbiased decisions is an extremely important aspect of business. This is especially true in industries such as banks/financial institutions, healthcare providers, and the court system, where the outcomes of these decisions affect every person in the community. 

The rising costs of artificial intelligence technology development require businesses to develop detailed plans for their technology implementation, including evaluations of their investments and anticipated returns, as well as methods to involve their employees. Organizations need to develop artificial intelligence systems that function as extensions of human skills rather than creating operational challenges or posing unexpected dangers.  

Preparing for Long-Term Growth  

The trend of rising AI model prices indicates that organizations must develop more effective planning methods alongside long-term funding strategies. Organizations planning to expand their AI operations should invest in efficient AI development to gain a competitive edge through advanced reasoning models.  

Organizations need to invest in infrastructure that can scale their operations, develop skilled workers, and create efficient workflows to manage operational expenses and maintain operational effectiveness. Organizations that plan for the future use AI as their primary technological enabler, enabling them to create new products, improve their work processes, and make better business decisions across their entire organization.  

Future Outlook  

AI advancements will impact pricing model systems, from enhancing computational methods to decreasing both the number of computers needed for model applications, thus reducing power consumption, to enhancing model-building performance, lowering the amount of resources required through improved processors, greater efficiencies in algorithms, and the use of distributed computing over time but also increasing the amount of computational capacity needed to operate, resulting in additional costs for organizations on these systems.  

As organizations implement rapid efficiencies in AI technology while maintaining performance levels, they will remain competitive in the fast-paced world of technological change. 

Conclusion: AI as a Core Business Investment  

The increase in AI model costs demonstrates that advanced reasoning skills have become essential for current business operations. Companies are increasingly viewing AI as an essential operational and strategic asset, requiring substantial financial investments.  

The process of implementing AI into business operations requires companies to manage three main elements: high-performance AI expenses, anticipated financial benefits, and the ethical standards required. The trend demonstrates that AI has become an essential component that drives innovation, improves operational efficiency, and enhances business competitiveness. 

Source: https://www.sec.gov/ 

AI-Powered Grids Emerge to Prevent US Data Center Shortages

NVIDIA emphasises AI-based energy grids now designed to handle increased electricity demand from data centers across the United States. Increased use of Artificial Intelligence Workloads are currently placing strain on energy infrastructure, which must support high-performance computing environments. Utilities and technology providers are combining their resources with AI and grid management to optimise power distribution and increase efficient cloud services. Cloud Services.  

Rising Energy Demand from AI  

In recent years, AI has been growing rapidly across a range of applications, especially in large-scale models and real-time analytics. The data centers that support these applications require ongoing, high-density power to run their GPU processors, power the host servers, and run their cooling equipment, leading to new issues with power supply from energy providers.  

Grid systems were not designed to handle concentrated, constantly fluctuating demands on the energy supply from each data center; therefore, many areas with a high concentration of data centers are experiencing significant pressure on their infrastructure, with concerns over limited capacity and potential electricity shortages. As these new challenges emerge, AI-driven grid systems offer a more effective way to manage this complexity.  

Intelligent Grid Optimization  

ML algorithms will enable AI-governed electrical grids to provide real-time analysis of energy consumption and how much energy will be supplied to customers for how long into the future. AI can utilise many disparate sources of data simultaneously, including energy consumption from sensors deployed throughout an electrical grid, such as past weather, consumption trends, and current weather conditions. 

Once all the data has been assessed, AI will provide an opportunity for immediate changes to power distribution to maximise efficiency/maximum effectiveness. In addition to providing universities with additional insight into available power sources and weather conditions, AI will enable them to optimise their fossil fuel and renewable portfolios to minimise the risk of outages while maximising overall grid operational efficiency. 

AI will also be able to identify inefficient operations, detect outliers, and recommend appropriate changes to operations or procedures to increase operational efficiency and reliability. 

Supporting Data Center Expansion  

As businesses invest in AI infrastructure, a reliable power source is a major factor in deciding where to locate data centers. AI grids allow utilities to add new facilities while still operating their current systems without being overloaded.  

AI grids will predict how many resources are needed so that when data centers are full, utilities can provide enough power to keep them operating without problems. This capability is very important because the technology on which AI systems rely requires ongoing access to the computing resources needed to deliver services at the expected level.  

Enhancing Energy Efficiency  

A main objective of AI-enabled grid systems is energy efficiency; by reducing waste and optimising power use, they help lower operational costs and minimise the environmental footprint. AI can help identify opportunities to improve energy generation and consumption efficiency, resulting in more efficient infrastructure.  

AI can also help to coordinate renewable energy sources, such as solar and wind, with the traditional electrical grid. This will help reduce reliance on fossil fuels and support overall sustainability efforts, especially as data centers expand their energy consumption.  

Real-Time Monitoring and Automation  

AI-based grids depend upon real-time monitoring to provide stability and efficiency. Continuous data collection/insight into how the grid operates enables AI systems to provide real-time translations into instant responses based on changing requirements or supply.  

Automation is also critical for rapid decision-making through computer-generated actions that supersede human input. The ability to automatically adjust to changing conditions is very important in high-demand situations, as this type of decision-making can help avoid outages and keep outage time to a minimum. In addition, automated systems will enable AI systems to respond to grid changes within milliseconds.  

Addressing Infrastructure Constraints  

Numerous power grids worldwide struggle with capacity and flexibility constraints and have great difficulty adapting to the ever-increasing demand from data centers. AI has enabled improvements to existing infrastructure and systems without requiring significant physical modifications or upgrades.  

In a similar manner to using data collection and analysis to optimise existing resources, utilities can use AI to improve the efficiency of their existing resources, thereby deferring or minimising providing the energy infrastructure to evolve alongside both technological and energy industry advancements.  

Collaboration Between Tech and Energy Sectors  

Working together, technology companies, energy providers, and policymakers will build AI power grids; NVIDIA is one example of how advanced computing helps build smarter energy systems.  

The partnership with all three types of organisations will allow them to use artificial intelligence technologies while managing the grid and to develop solutions for organisations to respond collaboratively to the new, complex issues arising from today’s energy needs.  

Economic and Market Implications  

The adoption of Artificial Intelligence (AI) grids can also have a significant impact on the economy by reducing operational expenses in data centers and utilities and increasing the use of AI-based applications and digital services. 

As demand for AI-based infrastructure continues to grow, the investment in intelligent grid technology will also increase. Companies that are defined as leaders in the provision of AI-powered infrastructure and intelligent grids will have a competitive advantage and can secure their leadership in the intersection of energy and technology.  

Future of Energy Management  

The development of AI-enabled grids will be a move towards more adaptive, intelligent energy systems. As technology continues to evolve, we expect new capabilities to be added to these grids, such as predictive maintenance and advanced forecasting, along with the integration of smart city infrastructure.  

As societies become increasingly dependent upon digital technologies, managing complex energy networks will be key to maintaining a functioning society. AI-powered solutions will enable the development of resilient, sustainable energy systems that foster future innovation.  

Challenges and Considerations  

While AI-enabled grids exhibit significant promise, they also present challenges (e.g., data and operational security, system interoperability, and compliance with relevant regulations). To build trust among stakeholders in AI systems, it is important that they operate safely and transparently. Integrating new technologies into existing infrastructure will require careful planning, funding, and investment from utilities. Further, utilities must weigh the trade-offs between innovation and reliability to avoid service disruptions and minimise instability during transitions.  

Conclusion: Powering the AI Era  

With AI-enhanced grids, data centers will have an entirely new option for meeting their current energy consumption needs. AI grids can leverage ML models and real-time analytics to improve how data centers manage energy, ultimately enhancing capacity and reliability and enabling better scalability. AI continues to accelerate industry growth, and the ability to provide consistent, reliable, and sustainable energy will play an important role in the future of technology development. AI-enhanced grid systems offer a significant opportunity to ensure the underlying infrastructure supporting digital advancements remains available and able to sustain the continued growth of technology. 

Source: https://nvidianews.nvidia.com/news/energy-ai 

CISA Warns of Ransomware Targeting US Critical Systems

CISA, the FBI, and international partners have issued urgent alerts warning that ransomware groups such as Phobos, Rhysida, Black Basta, and Play are aggressively and continuously targeting US critical infrastructure. These attacks, which frequently use double extortion, now threaten essential sectors such as water, energy, healthcare, and manufacturing by actively exploiting vulnerabilities, misconfigured remote desktop protocol (RDP) services, and virtual private networks.  

Key Threats and Targets 

  • CISA highlights ongoing ransomware threats targeting critical infrastructure, requiring urgent attention.  
  • Threat actors are focusing on water, energy, health care, public health, and manufacturing, underscoring the need for vigilance.  
  • Attackers are quickly exploiting compromised credentials, VPN vulnerabilities, and internet-connected programmable logic controllers (PLCs), posing an imminent threat.  

Recommended Mitigations 

CISA urges organizations to promptly implement these ransomware defenses.  

  • Enable multi-factor authentication (MFA) for all services, especially webmail, VPNs, and critical systems.  
  • Restrict RDP use, check for exposed ports, and secure VPNs.  
  • Keep offline encrypted backups of your data and test them regularly.  

Review stopransomware.gov guidance and report incidents to CISA or your FBI field office.  

This joint cybersecurity advisory is part of the ongoing #StopRansomware campaign, which provides network defenders with updates on ransomware variants and threat actors. These reports share both recent and historical tactics, techniques, and procedures (TTPs) and indicators of compromise (IOCs) to help organizations defend against ransomware. For more advisories and free resources, visit stopransomware.gov.  

Note: This advisory was originally published on December 18, 2023. Updates with dates are below:  

  • June 4, 2025, update. This advisory now details new tactics used by the Play Ransomware Group as of early 2025 and provides updated indicators of compromise to improve threat hunting. Updated IOCs have been removed.  

Update June 4 2025 

The FBI, CISA, and the Australian Cyber Security Center (ASDs, ACSC) are releasing this joint advisory to share indicators of compromise and tactics identified by the FBI as recently as January 2025, for the Play ransomware group.  

End Update 

Since June 2022, the Play Ransomware Group, also known as Play Crypt, has impacted many businesses and critical infrastructure across North America, South America, and Europe. Play was among the most active ransomware groups in 2024.  

To reduce the risk of playing ransomware, organizations should take these steps.  

  • Prioritize remediating non-exploited vulnerabilities.  
  • See guidance above on enabling MFA for webmail, VPNs, and critical accounts.  
  • Keep software up to date and run regular vulnerability assessments.  

Update June 4 2025 

As of May 2025, the FBI was aware of about 900 organizations targeted by these ransomware attacks.  

End Update 

In Australia, the first reported Play ransomware case occurred in April 2023, and the most recent occurred in November 2023.  

Play Ransomware is a closed-group setup to ensure the secrecy of transactions, according to its leak website. They use double extortion: data theft followed by encryption. Victims receive extortion letters with no specific payment instructions and must contact the task us by email.  

Update June 4 2025 

Each target receives a unique@gmx.de or web[.]de email. Some are threatened with data release and pressured to pay.  

End Update 

The FBI, CISA, and ASD’s ACSC urge organizations to implement the mitigations outlined in this advisory to reduce ransomware risk and impact. Key steps include using multi-factor authentication, maintaining offline backups, having a recovery plan, and keeping all systems and software up to date.

Source: StopRansomware: Play Ransomware 

NASA Reveals Saturn Data Showing Major Atmospheric Shifts

NASA and other research institutions have been studying Saturn’s behaviour and the composition of its atmosphere. The atmospheric properties of Saturn will provide scientists with essential knowledge to forecast weather on other celestial bodies, study how atmospheric systems develop, and test theories of planetary formation and evolution. 

Uncovering Atmospheric Changes  

Fresh data from Saturn shows significant modifications to its gaseous envelope, particularly when looking at the spread of temperatures across the globe and how clouds appear throughout Saturn’s atmosphere. Jet streams have fluctuated dynamically, including very vigorous storm systems, demonstrating that Saturn has undergone changes in its planet-wide atmospheric cycles over time, in conjunction with jet streams and storm systems.  

These unique phenomena have important implications for understanding how gas giant planets operate, given the complexity of the interactions among heat generated within the planet, solar energy reaching the planet, and chemical reactions in its atmosphere. The new information gathered gives researchers an extensive view of how various inputs (both internal and external) contribute to the overall development of gas giant planet weather systems on a grand scale.  

Shifts in Storm Activity  

The latest data highlight differences between storms across Saturn’s atmosphere and provide scientists with new insights into how they function. Researchers can track changes in the intensity and frequency of large-scale storm systems on Saturn, as well as how these storms have changed over time.  

These findings demonstrate that the long-term cycles affecting Saturn’s atmospheric behaviour their understanding of how energy flows on Saturn and how the various atmospheric systems change over time. Additionally, these findings create opportunities for comparison between Saturn’s weather systems and those of other worlds, such as Jupiter and Earth.  

Temperature and Chemical Variations  

Temperatures and chemistries throughout the planet Saturn’s atmosphere are shown to be greatly varied in the data as well. Several gases, including hydrogen, helium, and other trace gases, undergo changes in atmospheric chemistry as their molecular states shift. Changes in atmospheric chemistry can have an impact on the formation of clouds and energy transfer within Saturn’s atmosphere.  

Data shows differences in temperature across layers. This will inform scientists in determining how internal heat, as well as external sources such as solar energy, contribute to these temperatures, providing a more accurate model of how gas giants maintain equilibrium and behave under changing conditions.  

Implications for Planetary Science  

Saturn’s atmosphere has undergone significant changes, with broader consequences for planetary science, particularly for understanding gas giant exoplanets within & outside our solar system. Studying Saturn enables scientists to build models applicable to similar gas giant exoplanets.  

The data from this research may contribute to models that address how gas giants form & evolve, and how their atmospheres evolve. This information is critical in understanding what is happening in distant planetary systems and will help to explain the many differences between planets.  

Advancing Observation Techniques  

Technological innovations and improved data analysis techniques are now providing valuable insights into Saturn’s atmosphere. For example, high-resolution imaging, spectroscopy, and long-term monitoring now enable scientists to monitor changes in Saturn’s atmosphere more precisely than ever before.  

Using these technologies will allow researchers to observe small-scale changes in atmospheric activity, thereby gaining new insights into how planets behave. Technology improvements should further aid understanding of gas giant planets and other celestial bodies, creating more opportunities for planetary science research.  

Comparing Saturn with Other Planets  

A major benefit of Saturn’s atmospheric dynamics is the insight it provides into other planets in the solar system. Gas giant Jupiter has an atmosphere comparable to Saturn’s, but because of differences in size, composition, and energy sources, the atmospheres exhibit very different characteristics.  

Finding and studying these differences among planets helps identify basic principles or rules governing planetary atmospheres and also highlights the unique characteristics of each planet’s atmosphere. us conditions.  

Long-Term Climate Cycles  

There may be significant atmospheric trends reflecting long-term climate cycles spanning multiple years. These long-term variations can be extremely difficult to discern solely from short-term climate observations, without accounting for the full spectrum of climate change.  

A way to better understand these long-term cycles is to accumulate long-term observations until we begin to develop an understanding of the structure of atmospheric variability. The accumulation of extensive datasets allows scientists to develop predictive capabilities for future trends and better understand the forces driving atmospheric variability. Ultimately, this knowledge is essential to the development of accurate models of planet-wide climates and their development over time.  

Impact on Exoplanet Research  

The recent discovery of Saturn has implications for all planetary systems of the universe. Hundreds of new exoplanets have been discovered in the last few years, and most are gas giants like Saturn, with atmospheres very similar to Saturn’s. Therefore, by applying data from Saturn, scientists will be able to enhance their interpretation of discovered exoplanets.  

This link between nearby and distant planetary systems underscores the importance of using our solar system as a basis for studying planets from an astronomical perspective, both near and far.  

Future Exploration and Research  

NASA intends to continue using both observational missions and analysis of existing data to extend its existing research on Saturn and other gas giants in our solar system. Future missions may reveal much more precise information than previously gathered about atmospheric composition, weather patterns, and the internal structure of these planetary bodies.  

Ongoing efforts will continue to provide accurate models, broaden our understanding of the universe’s fundamental processes, and answer new questions raised by current information. This research will require collaboration among national and international space programmes.  

Looking Ahead: Expanding Planetary Knowledge  

The new information from Saturn has vastly improved our understanding of planetary atmospheres. The new data show that the atmospheres of planets can change over time and that many different processes take place within those atmospheres, leading researchers to realise that earlier theories about the behaviour of gas giant planets may not be accurate, thus providing new areas for further study.  

Ongoing investigation of this data should yield additional research findings that contribute to our current knowledge base on gas giant planets and planetary systems.  

Conclusion: A Dynamic View of Saturn  

NASA’s recent findings demonstrate that Saturn is a constantly changing planet with atmospheric processes far more complex and intricate than we originally thought. The major changes in Saturn’s atmosphere demonstrate that there is still much more to discover about our solar system through continued exploration and analysis of data.  

The findings of this study will not only expand our knowledge of Saturn but also further our scientific understanding of how planets work, how climates develop, and the behaviours of other worlds in our universe. 

Source: https://www.nasa.gov/news-release/ 

Anthropic Builds AI System to Auto-Patch Software Flaws 

Anthropic has built AI systems that independently find and fix software flaws, including serious zero-day vulnerabilities. Claude Mythos is so advanced that its public release is limited to prevent misuse.  

Key developments include:  

Claude Mythos (High Risk/High Capability) 

  • Performance: In controlled evaluations, Claude Mythos identified thousands of security vulnerabilities in major operating systems and web browsers, including undiscovered flaws persisting for over 25 years.  
  • Autonomy: the model autonomously chains multiple exploit types, such as JIT heap spraying and sandbox escapes, into a single exploit. To achieve system-level access without human intervention.  
  • Access restriction: because it could be used for serious cyber attacks, Anthropic is not making its thoughts available to the public.  
  • Anthropic shares Mythos with select companies, such as Google and Apple, to enhance security.  

Claude Code Security (Production Tool) 

  • This tool, now in research preview, scans code for security issues and suggests fixes.  
  • It targets subtle context-dependent vulnerabilities such as business logic errors that are frequently overlooked by conventional static or dynamic analysis tools.  
  • The tool reviews pull requests, flags bugs before code merges, and shares summary comments identifying code issues.  

Impact and Security Risks 

Claude found 22 vulnerabilities in Firefox with Mozilla in two weeks, nearly a fifth of high-severity bugs fixed in 2025. Anthropic says this tool helps companies fix bugs faster and at scale, outpacing human teams. Anthropic warned that attackers could use the tool to exploit zero‑day vulnerabilities, thereby restricting access to those Mythos.  

Anthropic’s code review feature finds bugs in software before code is merged and is now part of their coding platform. Claude Code is available as a beta research preview for team and enterprise users.  

AI Agents to Review Code Changes 

Code review checks pull requests, which are how developers submit and review code changes before adding them to the main project.  

Anthropic says the tool uses multiple AI agents simultaneously to review code changes, spot potential bugs, and eliminate false positives. The results are shared in a single summary comment on the pull request, along with additional comments indicating severity. Red for critical, yellow for concerns, and purple for existing bugs.  

Designed to address growing delays in code reviews, Anthropic built this tool to keep pace as AI speeds up development.  

Code review slows development; customers report similar issues. Developers are overextended, so many pull requests only get brief reviews. Cat Wu, head of product, says the feature targets logic errors and offers actionable feedback, addressing frequent criticism of prior AI tools.  

How The System Works 

Upon a pull request, the AI orchestrates several agents to simultaneously inspect the code base from diverse technical perspectives. The coordinating agent then aggregates ranks by severity and de-duplicates the findings for final delivery.  

The system explains its reasoning step by step, showing what the issue is, why it matters, and how it could be fixed.  

Anthropic said the extent of analysis scales with the size of the code update; large or complex changes receive more extensive reviews while smaller updates undergo lighter checks. On average, a review takes around 20 minutes.  

84% of large pull requests had issues (average 7.5 per pull request). 31% of small ones have issues (average 0.5). Engineers usually agreed with the findings; fewer than 1% were found to be incorrect.  

Code review uses a token pricing model, typically costing $15–$25 per pull request. Admins can set monthly spending limits, adjust repositories, and monitor review activity and costs via dashboards.  

Source: Anthropic launches AI-powered Code Review tool to detect bugs in pull requests 

Meta Shuts AI Cost Tracker After Exposing Model Expenses 

Meta has ended its partnership with Mercor, a $10 billion AI data startup, after a supply chain attack exposed some of the AI industry’s most closely guarded secrets. The breach, which began with a compromised open source library in late March 2026, revealed not only personal data but also the training methods behind top language models. Hackers used a tampered version of the LiteLLM open-source library, leading to investigations at OpenAI and Anthropic and a class-action lawsuit involving over 40,000 people.  

Last month, in March 2026, hackers targeted a popular open-source library and stole more than just personal data. Wired reports that they may have also taken the blueprints for building some of the world’s most advanced AI models.  

This disruption follows a major cybersecurity breach. Meta has put its partnership with Mercor, a San Francisco AI data company, on hold after a March 5, 2026, cyberattack exposed private details about Mercor and possibly its clients. The pause has no set end date and has made many in the industry nervous since companies have spent billions developing these Secret methods.  

The Startup Behind the Curtain 

Mercor may not be widely known, but it plays a key role in the AI industry. Founded in 2023 by Brendan Foody, Adarsh Hiremath, and Surya Midha, three friends from the Bay Area, the company brings together contractors, engineers, lawyers, doctors, bankers, and journalists to create high-quality training data for AI labs. Its clients include Meta, OpenAI, Anthropic, and Google.  

Mercor’s growth has been remarkable, even in October 2025, for Silicon Valley. It raised $350,000,000,000 in a Series C round, reaching a $10,000,000,000 valuation and making its three founders the world’s youngest self-made billionaires at 22. By September 2025, the course’s annual revenue hit $150,000,000 up from $100,000,000 just three months ago. Its focus on creating fine-tuning and reinforcement learning data for AI labs has made it one of the most valuable private companies in the AI supply chain.  

However, Mercor’s position at the center of the air supply chain has come with risks.  

A Poison Package, a Cascade of Exposure 

The attack on Mercor started further up the supply chain in late March 2026. Wiz, Synk, and Datadog Security Labs found that a hacker group called TeamPCP broke into the CI/CD pipeline of LiteLLM, an open-source Python library. LiteLLM is used by millions of developers and has 97,000,000 monthly downloads, and is found in about 36% of cloud environments.  

Earlier, TeamPCP used a supply-chain attack against Trivy, a popular security scanner, to steal traditional credentials. On March 27, 2026, a light ML LLM maintainer used these credentials to upload two malicious versions of LiteLLM 1.82.7 and 1.82.8 to the Python Package Index (PyPI). The harmful packages were available for about 40 minutes before they were found and taken down.  

The attack was complex. Version 1.8.7 hit base64-encoded malware in the library’s proxy server code, which ran as soon as it was imported. Version 1.8.2 used a harmful path configuration file that triggered every time a Python process started. Both versions were made to steal environment variables, API keys, SSH keys, cloud credentials for AWS, Google Cloud, and Azure, CI/CD secrets, and database authentication, and to send all the stolen data to a server at app. models.litellm[.]cloud.  

Mercor confirmed it was hit by the attack. Later, they discovered nearly 4 terabytes of exposed data. This included platform source code, a large user database, and video interviews with identity documents. The breach may have revealed the names and social security numbers of over 40,000 contractors and customers.  

The Secrets That Matter Most 

The exposure of personal data is serious, but Meta and other AI labs are more concerned about leaked proprietary information.  

Because Mercor manages data pipelines for multiple AI companies, the bridge may have developed expertise and confidential data-processing and training strategies over the years of investment. Unlike datasets, these methods have critical advantages. Several AI labs are investigating the extent of the leak.  

OpenAI is reviewing the incident but hasn’t stopped working with Mercor. Anthropic hasn’t commented publicly. Google is also believed to be assessing the impact of the breach.  

This bridge reveals a major industry risk column when many companies rely on a single supplier. A single bridge can compromise the top AI methods across the sector.  

Extortion and Legal Fallout 

The hacker group Lapsus$, known for past attacks on big companies, claimed responsibility for the NORCO breach and started selling the stolen data on dark web forums. Security experts believe Lapsus$ is working with the team PCP, which has become a major threat in the AI and enterprise software worlds. This group is also believed to be behind a series of supply chain attacks that hit over 1,000 enterprise SaaS environments, including a breach of the European Commission linked to the same campaign by CERT-EU.  

On April 1, 2026, plaintiff Lisa Gil, a resident of Wahiawa, Hawaii, filed a class action complaint against Mercor.io Corp. in the U.S. District Court for the Northern District of California. The suit alleges that Mercor failed to maintain adequate cybersecurity protections, leaving more than 40,000 people exposed to identity theft and fraud. The complaint states that a LiteLLM incident on March 27 was the entry point. It also claims that Mercor’s reliance on a compromised open-source dependency, without sufficient monitoring, created dangerous conditions that led to the breach.  

Meta has not made any public statements about the breach. In March 2026, the company signed a $27,000,000,000 AI infrastructure deal with Nebius Group and experts, spending between $135,000,000,000 and $150,000,000,000 this year, making its air training pipeline extremely important. Stopping work with a T beta vendor is a decision Meta would make only if their secret methods outweighed the cost of hunting operations.  

A Cautionary Tail For The AI Supply Chain 

The Mercor highlights how modern supply chain attacks can expose both credentials and unique intellectual property when AI companies depend on the same data vendors and open-source tools.  

Security companies have warned about this exact problem. Aikido Security, which became a unicorn in January 2026, is based on the idea that open-sourced dependency risk is a major threat to enterprise software. The Mercor breach shows that this risk may be even greater for the AI training pipeline.  

The next few months of 2026 will show whether Mercosur’s rapid growth can continue after a March breach that compromised both user data and clients’ most closely guarded secrets. The AI industry’s rapid pace in 2025 was driven by the belief that this infrastructure was secure. Now, that belief is being questioned.  

Source:  Meta freezes AI data work after breach puts training secrets at risk  

Caltech Enables Micro-metal Printing for Next Gen Wearables 

Caltech researchers have developed a novel 3D printing technology called Hydrogel Infusion Additive Manufacturing (HIAM). This process enables the fabrication of microscale, intricately detailed metallic structures much smaller than previously possible. It could advance the development of high-performance wearable technologies, biomedical devices, and microelectromechanical systems (MEMS).  

Highlights Of Caltex Micro-Metal Printing 

  • HIAM begins by additively manufacturing a hydrogel template, which is subsequently infused with metal precursor ions such as copper or nickel.  
  • The construct then undergoes dual-stage thermal processing. This eliminates the polymer matrix, sinters the metal, and isotopically shrinks the component by up to 90%, yielding dense, miniaturized, and highly precise features.  
  • Microscale metallic components produced in this manner often exhibit nanoscale porosity and grain boundary networks. Surprisingly, these microscopic structural defects yield parts that are three to five times stronger and more fatigue resistant than those of conventional alloys.  

How This Affects Wearables and Smart Technology 

  • This process facilitates the fabrication of miniaturized metallic devices, such as microelectromechanical sensors and actuators, which are critical components of microphones in advanced wearable systems.  
  • Caltech is collaborating with Meta Inc to apply this research toward the development of new augmented reality and virtual reality wearable devices.  
  • This technique also enables the fabrication of flexible, reconfigurable electronic devices, including biosensors and physiological monitoring.  

The research group led by experts such as Julia R. Greer has demonstrated print resolution to 150 nanometers comparable to the size of the influenza virion. This capability suggests applications in ultra-precise biomedical implants and sensing platforms.  

These advancements are enabled by a water-based process developed by Caltech engineers, as highlighted in a recent Nature paper published on October 20.  

This new method works with many metals. The additive manufacturing approach is compatible with multiple metals and alloys and permits compositional gradients within a single manufactured part with marginal modification. It presents potential for miniaturized components in microelectronics, transportation, aerospace, thermal management, and medical fields. Manufacturing creates items layer by layer. This allows for shapes that conventional techniques, such as forging, molding, etching, or milling, cannot make. Most current 3D metal printers use lasers to melt metal powders, achieving detail down to about 100 microns, roughly the thickness of two sheets of paper. Resolution refers to the smallest detail the process can produce.  

One challenge is that metals like copper conduct heat very well. Even with a focused laser, the heat spreads and melts powder outside the intended area, reducing the level of detail achievable.  

To overcome these barriers, Caltech’s team, including Max Saccone, Rebecca Gallivan, Daryl Yi, and Kai Narita, took an innovative path. Rather than print materials directly, they used hydrogels as scaffolds for metal-containing liquids. Kai Narita has since founded 3D Architect to commercialize this technology licensed from Caltech.  

“We need to develop a different approach as we cannot rely on heat to construct our structures,” said Saccone.  

Hydrogels are made from flexible polymers. Hydrogels composed of hydrophilic polymer networks remain intact in aqueous environments and are used in products such as soft contact lenses. Ultraviolet photopolymerization induces crosslinking in liquid polymers, forming solid structures in prescribed patterns. Immersing hydrogels in metal ion solutions results in uniform volumetric ion loading. Calcination at 700 to 1,100 degrees Celsius, depending on the metal, combusts the polymer matrix. Since the metal’s melting point exceeds the calcination temperature, metallic integrity is preserved. The hydrogel also causes the entire piece to shrink, resulting in an even smaller metal part. This method allows the team to 3D print pure metals, alloys, and mixed metal systems with features as small as 40 microns, which is less than half the width of a human hair. They have printed structures from copper, nickel, silver, and various metal alloys.  

The hydrogen infusion additive manufacturing process, or HIAM as we coined it, establishes a means to create metallic materials in an entirely new, much more environmentally friendly way at unrivaled precision levels, says Greer, Ruben F., Donna Mettler Professor of Materials, Science, Mechanics, and Medical Engineering, and Director of the Kavli Neuroscience Institute.  

The research received funding from the US Department of Energy, the Resnick Sustainability, the Massa On Foundation, and Caltech’s AI4science initiative. 

Source: New Process Allows 3-D Printing of Microscale Metallic Parts  

NVIDIA CloudXR Streams RTX Graphics To Spatial Devices

NVIDIA CloudXR 6.0 lets you stream high-quality, RTX-powered graphics to devices such as Apple Vision Pro, Meta Quest 3, Pico for Ultra, and web browsers. As a universal OpenXR bridge, it delivers real-time, photorealistic rendering from remote workstations or cloud servers, giving you untethered, high-quality XR experiences.  

Key Features and Benefits of CloudXR 

  • CloudXR 6.0 streams high-fidelity content to spatial devices and web browsers, including Apple Vision Pro, Meta Quest 3, Pico, and others.  
  • CloudXR 6.0 provides native support for Vision OS, leveraging dynamic foveated streaming to deliver 4K output with minimal latency while preserving user data privacy.  
  • As a universal bridge, CloudXR 6.0 enables developers to build XR apps once and deploy them across platforms such as iOS, iPadOS, and visionOS.  
  • CloudXR 6.0 shifts computationally intensive rendering from XR devices to high-performance workstations. This enables efficient streaming of complex 3D data assets and reduces on-device processing requirements for headsets.  
  • CloudXR.js enables developers to deliver interactive, GPU-rendered 3D content directly to XR device browsers, such as on Apple Vision Pro, Meta Quest 3, and Pico 4 Ultra, leveraging real-time streaming protocols.  

CloudXR 6.0 lets professionals in disciplines like automotive design and healthcare experience interact with, and work together on complex 3D models from anywhere.  

NVIDIA CloudXR 6.0 is a GPU-accelerated streaming platform that brings high-quality spatial experiences. From powerful GPUs to a wide range of AR, VR, and spatial computing devices, the following section explains how its architecture delivers these experiences. With full OpenXR compliance, developers can build once and deploy to any supported headset or operating system. By removing the need for powerful local hardware, the SDK lets you stream photorealistic digital twins and elaborate simulations to lightweight devices, including native support for Apple Vision OS and easy web access through CloudXR.js.  

How CloudXR Works 

NVIDIA CloudXR 6.0 serves as a universal open XR bridge, offloading processing from the XR device. It delivers photorealistic spatial experiences through three core components:  

  • CloudXR runtime (server): This component runs on Windows or Linux workstations and handles GPU-accelerated rendering and low-latency XR content encoding. It connects RTX-powered applications to the network interface for delivery to client devices.  
  • CloudXR frameworks (client) for Apple platforms. There are two ways to build apps that receive CloudXR streams on visionOS. You can use the Foveated Streaming Framework to stream high-quality OpenXR applications. This system sends top-quality content only where it’s needed, based on the user’s location, to maintain high performance. On iOS or iPadOS, you can use StreamingSession.xcframework to stream OpenXR experiences to iPhones or iPads. Both libraries have similar APIs, making it easy to build cross-platform streaming apps.  
  • CloudXR.js (Web Clients) This JavaScript framework makes browser-based XR easy. Devices such as Apple Vision Pro, Meta Quest 3, Pico 4, Ultra, and other supported platforms can access cutting-edge robotics, Omniverse, and open-edge XR content via WebRTC, with no need to install anything from an app store.  

Get Started With Cloudxr 

CloudXR Runtime Server SDK for OpenXR 

Deploy the essential server-side engine to render, encode, and stream your NVIDIA RTX–powered applications. CloudXR Runtime 6.0 works as a standard OpenXR bridge, so any compliant application from NVIDIA, ISAC, Lab, to custom engines can stream photorealistic content to lightweight wireless clients with ultra-low latency. 

Source: NVIDIA CloudXR