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As we enter the next era, what will be the defining measure of our progress?

Every industry has a word that shapes how it thinks. For pilots, it’s safety. For insurers, it’s risk.

For the semiconductor industry, it’s yield.

Yield does not ask how elegant the solution is, how many years it took or what the roadmap promised. Rather, it asks one simple question: What useful output did we produce?

For more than 60 years, the semiconductor industry has asked that question, relentlessly maximizing the number of usable chips produced from every wafer. Generation after generation, wafer after wafer, it is precisely that discipline that turned the transistor from a laboratory curiosity into the foundation of modern life.

Today, we need to apply the same principle to the unprecedented resources that the world is pouring into AI: capital on a scale once reserved for nations, gigawatts of power and record-breaking fabs and datacenters.

The question that will define this decade is the same one this industry has always asked: What actually comes out? Not just chips and tokens, but as affordable intelligence, as work that matters and as outcomes that improve lives.‌

That is the yield imperative.

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Click here to load media The limit of more 

AI has proliferated with remarkable speed, at a rate of adoption faster than the internet, the PC or even the smartphone. Yet, global penetration still stands at just 18% of the working population, and the vast majority of that usage is chat-based. As systems move from answering individual prompts to reasoning, planning, using tools and executing longer agentic workflows, the infrastructure equation changes dramatically. A single agentic task can use more than 3,400 times as many tokens as a typical chat interaction.

We are only in the early innings of agentic adoption, and the infrastructure is already strained. Power is setting the limits on what we can build and when. Packages and racks are growing larger and denser. Memory is becoming an even tighter constraint.

For years, the industry’s rational answer to each new requirement resulted in more: more silicon in the package, more memory beside it, more power to feed it and more fiber to connect it. Each generation delivered meaningful progress. But when each new gain requires more input than the one before it, we are on a treadmill. It moves only as long as we keep adding to it.

I believe we need to pursue two paths forward. The first is evolutionary: we continue improving the architectures we have today, driving incremental efficiency, utilization and economics within each generation. The second is transformational: changing the curve itself with innovation in new architectures, new materials and new approaches to system and model design.

The history of our industry is defined by transformations like these. When increasing CPU clock speeds ran into the power wall, we moved to multicore processors. When planar NAND reached its limits, memory went vertical.

And now, once again, we have an opportunity to challenge our assumptions and rethink the fundamentals. Because the next chapter of AI won’t be defined simply by how much infrastructure we build, it will be defined by how much intelligence we can create from it.

For decades, the computing industry has optimized yield in the context of manufacturing. Today, that discipline has to extend across layers, from datacenters and silicon through models and the agentic harnesses that orchestrate them. And the work does not stop once the technology is built. We must then deploy and scale it faster, while developing new tools and systems to maximize utilization across our fleet.

From development through execution, each layer has a yield of its own, and losses and gains compound across them. Capacity at any one layer is only a starting point. The real measure is how effectively those layers work together to produce useful output from the system as a whole.

Our experience at Microsoft building and operating AI infrastructure at scale has reinforced two key lessons. First, the biggest constraints are rarely solved in the layer where they appear. Second, when we attack a constraint across the whole stack, tradeoffs that seemed inherent to the problem often turn out to be artifacts of the architecture.

The greatest advances often come when we apply these learnings through co-design, working across layers to turn apparent limits into solvable system constraints. Innovations in memory, networking and power show what this approach looks like in practice.

Today, memory is viewed as a supply problem or a component problem. In reality, it is a system problem.

In AI inference, memory is now setting the limits on system performance. It must hold larger models, preserve longer contexts and deliver data fast enough to keep the compute fed. And agents raise the bar even further. Generation, retrieval, tool use and persistent memory run together in loops that can last minutes or hours. The result is a much longer memory horizon, with far more information kept close to the compute and available across an expanding sequence of turns. Doing that efficiently at scale will define the next generation of AI infrastructure.

Our experience building the Azure Maia platform demonstrates that memory bottlenecks are not resolved by a single layer.

Model architecture, data science and compression can reduce the amount of KV cache, which stores the model’s working context during generation. Software can manage memory hierarchies more effectively, silicon can be optimized for data movement efficiency and compilers can place data closer to compute. No one change removes the constraint. Together, they increase the useful intelligence the system can deliver from the same memory resources.

That is useful yield: not simply adding bytes but getting more useful intelligence from every byte we already have.

As we zoom out to the cluster level, we see that intelligence does not come from one chip. It comes from thousands of chips operating as one system. Faster links matter, but the productivity of the system also depends on congestion management, failure recovery, workload placement, programming complexity and the boundaries between silicon, system and software. Together, they determine whether expensive compute is producing intelligence or sitting idle.

When architecting the platform for Maia, we did not begin with an existing networking design. We began with the outcome we wanted to deliver: efficient inference at fleet scale, designing across silicon, networking and system software. Instead of separate scale-up and scale-out fabrics, we built a two-tier scale-up network, integrated the NIC functionality directly into the chip and developed a custom transport layer.

The result is scalable, consistent performance across dense inference clusters, with a unified fabric that simplifies programming, improves workload flexibility and makes better use of available capacity. And with less network hardware needed to deliver this performance, we also lowered the cost of running the entire system.

Our objective is not merely to move data faster. It is to keep more compute productive and deliver more tokens from every watt and every dollar.

Moving from the cluster to the grid, AI has introduced new challenges around power availability, distribution and utilization. Racks have gone from tens of kilowatts to hundreds of kilowatts, and datacenter campuses can operate on the scale of gigawatts. Power used to be something the system simply plugged into. Now, it is something we design around, from the grid to the chip.

That is why the industry is rethinking power across the system. Solid-state transformers and 800-volt direct current power delivery can reduce distribution losses as power moves through infrastructure. Power and cooling are no longer downstream of the design, they are part of the product definition from the start. And increasingly, that co-design is needed all the way into the silicon.

Azure Cobalt 200, our Arm-based server CPU, shows what this looks like in practice. We designed Cobalt so that every core has its own voltage and frequency controls, paired with software-based, per-virtual-machine power capping. This finer-grained control enables targeted power adjustments while protecting the performance of critical workloads, allowing us to run more servers within the same power envelope.

As Cobalt demonstrates, hardware-software co-design enables us to more effectively turn every megawatt into customer value.

The pattern we see across memory, networking and power extends throughout the system: start with the useful output, then optimize the whole rather than any one layer. This first requires us to be precise about the output we are optimizing for and which design constraints are truly fixed. Are we optimizing for peak performance or sustained system throughput? Would the workload benefit from significantly more capacity with marginally less redundancy? What creates more value: a broader set of capabilities or significantly earlier customer deployment?

Not every constraint in today’s systems is a law of physics. Some are inherited from decisions made elsewhere in the system and can change only when we work across traditional boundaries. Evolution comes from the steady gains each company drives within its own domain, but transformation comes when we challenge those assumptions together and redesign the system as a whole.

The breakthroughs ahead will emerge from collaboration across the ecosystem, spanning hyperscalers and silicon providers, equipment makers and materials innovators, utilities and datacenter operators, model builders and software developers.

But tokens and intelligence are not the finish line. What we produce becomes the input for someone else’s work.

What matters next is how broadly that input translates into productivity across the economy and value in people’s lives, whether it helps a scientist accelerate discovery, a clinician identify a signal earlier, a student get help at the right time or a small business find a new path to growth.

That happens as AI becomes part of everyday work across industries and around the world. People build on it, new uses emerge, the tools improve and the value compounds.

For that cycle to spread, AI must be broadly accessible. And at scale, accessibility depends on efficiency. It is the only way to deploy enough intelligence, and at a cost that allows it to reach everyone.

When every person and every company can access intelligence, build on it and create value of their own, that is full yield.

We will continue to build capacity because the world will need it. But our defining measure of progress must be what comes out: not only chips or tokens, but useful intelligence translated into empowerment, opportunity and human achievement.

That is the yield imperative. And it is work our entire industry must take on together.

Rani Borkar leads the core organizations responsible for planning, architecting, developing and deploying hardware and infrastructure for Microsoft’s leading cloud computing platform — from silicon, to systems, to supply chain.

Learn more about Microsoft’s silicon to systems approach to Azure infrastructure.

The post The yield imperative: Turning AI infrastructure into useful intelligence appeared first on The Official Microsoft Blog.

Throughout this past fiscal year, customers across every industry and segment moved from AI experimentation to deploying AI for real-world business outcomes. They unlocked innovation and created new opportunities for growth. We saw the emergence of Frontier Firms as they moved beyond efficiency gains to focus on human ambition and embed AI at the core of how they operate. Successful customers are building an intelligence platform so their unique IQ — their knowledge, data, workflows, applications and expertise — can continuously compound, ensuring the value of AI accrues to the customer, not the model. They have a trust platform that is pervasive, with the ability to manage, govern, secure and measure AI across every business process.

Everything we are doing at Microsoft is empowering Frontier Transformation: Copilot enables AI in the flow of human ambition, Microsoft IQ amplifies and protects an organization’s IQ and Agent 365 is the trust platform that enables observability at every layer of the stack.

Businesses are not static, and neither are the AI systems that support them. As organizations evolve, AI systems must continuously learn and improve. Agentic workflows need to be built, observed and tuned against the outcomes organizations seek and the ROI they demand. Microsoft’s open, model-diverse and heterogenous platform powers that improvement loop. We also recently announced Microsoft Frontier Company, bringing our AI engineering approach to customers around the world to help them build these AI systems to accelerate measurable business outcomes.

Throughout the past year, we saw customers put these capabilities to work in powerful ways — embedding AI into core business processes, building agentic systems, strengthening security, accelerating innovation and creating new sources of value. The stories below highlight organizations leading Frontier Transformation, demonstrating how intelligence, trust and human ambition come together across industries.

To advance its journey to become a global AI-powered company, Atos Group deployed Microsoft 365 Copilot to 56,000 employees across 54 countries — from consultants to engineers to frontline workers — and was one of the first organizations globally to adopt Microsoft 365 E7: The Frontier Suite. Using Microsoft Foundry, Microsoft Copilot Studio and Agent 365, Atos is building, operating and governing a growing ecosystem of 19,000 AI agents through a unified operating model that brings together productivity, security, compliance and agent governance. As Atos embeds secure agentic AI across its workforce, the company is creating a repeatable model to continuously improve thousands of agents at scale while applying the same playbook to help customers accelerate adoption across highly regulated industries.

Facing state-sponsored threats and complex global operations, ASM is strengthening cyber resilience with Microsoft Security Copilot, helping protect the intellectual property behind advanced semiconductor manufacturing. By bringing threat investigations into a unified AI-powered experience, ASM enables analysts to investigate incidents faster, apply consistent decision-making across global operations and accelerate the development of cybersecurity talent. The company reduced incident triage time by 68%, cut laptop compromise investigations from 25 minutes to eight and now saves 337 hours each week on investigations while redeploying 20% of its security operations staff to governance, risk and compliance initiatives.

Banco Popular Dominicano, the largest private-sector bank in the Dominican Republic, transformed operational risk management from periodic, sample-based reviews into continuous, AI-powered supervision. Using AURA — an ecosystem of specialized agents built on Microsoft Copilot Studio and Microsoft Power Platform — the bank monitors 100% of its operational risk universe in real time, up from roughly 40% coverage, and can automatically analyze changes, validate controls and surface issues as they occur. The shift has delivered seven times greater analytical capacity, reduced manual operating effort by 70%, achieved 98% methodological accuracy and enabled continuous processing of approximately 80,000 documents per week and more than 300 cases per day. Just as importantly, risk teams have moved from reacting to problems after the fact to anticipating and preventing deviations before they occur. These results demonstrate the power of AI democratization. By enabling business teams to build intelligent solutions themselves through low-code tools, Banco Popular transformed operational risk management while fostering a culture of innovation led by domain experts.

To help reduce the manual burden for employees while meeting the pharmaceutical industry’s strict data security requirements, Cactus Life Sciences modernized scientific workflows with Microsoft 365 Copilot and agents. The company has deployed more than 30 custom automation agents to streamline document review and structure data extraction and information retrieval across scientific writing and project management teams. Supported by a centralized knowledge repository and the Copilot Champions community, the company reports efficiency improvements of approximately 35% to 50% in structured data extraction. By automating labor-intensive tasks and maintaining human review and quality controls, the company is enabling scientific writers to focus on deeper analysis, synthesis and delivering exceptional science to clients.

Chow Tai Fook is redefining luxury retail with Microsoft 365 E5, Microsoft Purview, Microsoft Azure OpenAI Service, Microsoft Fabric and Microsoft Foundry. The company has deployed over 400 customized AI agents supporting more than 24,000 employees, with millions of AI interactions each month and core business-process efficiency gains exceeding 70%. Through its AI Fook super-agent ecosystem, frontline associates can instantly access product expertise, inventory insights and personalized recommendations, helping drive sales conversion improvements of up to 57% while delivering hyper-personalized omnichannel experiences at scale. With hundreds of AI agents operating across the business, Chow Tai Fook is creating a foundation where customer, product and operational intelligence can be applied across every interaction, helping personalize experiences and improve decision-making across its global retail network.

To accelerate AI adoption, EY moved AI from experimentation into enterprise-wide transformation. After deploying Microsoft 365 Copilot to 150,000 employees and realizing a 15% productivity gain, the firm is expanding the Microsoft 365 Frontier Suite across its global workforce of more than 400,000 people, embedding agentic AI capabilities across the enterprise. As Client Zero, EY is applying Microsoft technologies across its own operations, including Microsoft Power Platform, Microsoft Copilot Studio, Microsoft Azure, Microsoft Foundry and Microsoft Fabric. The results include 95% faster lead times, a more than 37% reduction in finance operating costs and up to a 90% reduction in manual workloads across key business processes.

To reimagine the grocery shopping experience, Grandiose Supermarkets created an AI-powered shopping companion — GrandChef — built on Microsoft Foundry and Azure OpenAI Service. By connecting meal inspiration, recipe discovery and product purchasing into a single experience grounded in live product and inventory data, GrandChef helps shoppers move from intent to purchase faster and with greater confidence. This has led to a 31% increase in conversion, a 20% lift in average basket value and shopping journeys that are 40% faster. Together, these gains are helping Grandiose Supermarkets create more personalized customer experiences while driving measurable business growth. By connecting customer intent, product data and purchasing decisions in a single experience, Grandiose is creating a feedback loop that helps continuously improve recommendations and shopping experiences.

To support audits across its global organization, Grupo Bimbo built two agents with Microsoft Copilot Studio and deployed them through Microsoft 365 Copilot and Microsoft Teams: the result was Audit Assist, an idea generated from a Microsoft-supported internal hackathon and Comatrix. By connecting auditors directly to approved guidance, procedures and templates in SharePoint, the company is helping teams work more efficiently across 39 countries while improving consistency and audit quality. The solution/AI agents reduced planning-phase audit time by 20% and accelerated risk and control matrix creation from days to seconds, enabling auditors to spend less time searching for information and more time on analysis and decision-making. By making approved audit knowledge instantly accessible across its global audit organization, Grupo Bimbo is creating a foundation where expertise can scale with the business — improving consistency, reducing rework and enabling auditors to focus on higher-value analysis and decision-making.

To create a unified foundation for data, automation and AI across its operations in 47 countries, Navien built a connected, intelligent operating model with Microsoft Fabric, Microsoft 365 Copilot, Microsoft Foundry and Microsoft Copilot Studio. By connecting fragmented data and processes across procurement, manufacturing, quality and customer service, the company is enabling more consistent, data-driven decision-making across its operations. Navien saved 28,000 hours annually through AI agents and automation, with 32% of employees using self-service analytics to make decisions without IT support. Through its migration to Azure, it also expects more than 1.4 million in total cost-of-ownership savings over five years. By connecting data, insights and workflows across the business, Navien is enabling faster, more consistent decision-making at scale across its global operations.

NHS England is accelerating AI adoption across the healthcare system with the largest implementation of its kind in the healthcare sector. Following a trial involving 30,000 workers across 90 NHS organizations — where users saved an average of 43 minutes of administrative time per day — NHS England is rolling out Microsoft 365 Copilot to over 500,000 clinicians and support staff. Organizations can use Microsoft Copilot Studio to build and deploy AI agents that streamline clinical, operational and administrative workflows. Through Agent 365, NHS England can govern and scale those agents across the healthcare system while enabling individual trusts to build solutions for local needs — creating a secure framework where agentic workflows can be deployed, managed and expanded consistently to improve service delivery, reduce costs and create more time for patient care.

Novo Nordisk is using AI to help researchers make faster, more quantitative decisions in pharmaceutical R&D. Working with Microsoft’s AI Acceleration Studio within the Forward Deployed Engineering team, the company built a governed reasoning agent on Microsoft Azure with its proprietary dataset, including more than 200,000 patient-years of harmonized clinical trial data, while maintaining rigor, oversight and compliance. The system has expanded the team’s capacity to evaluate potential opportunities from 5 to 10 strong ideas per quarter to more than 50, and the company expects it to reduce time to insight for exploratory analyses from weeks to minutes. By grounding AI in its datasets and governed workflows, Novo Nordisk is helping scientists turn decades of institutional expertise into a reusable intelligence layer that can help accelerate discovery across the organization.

As AI becomes part of investment decision-making, SimCorp is helping financial institutions bring AI into investment workflows without compromising governance, auditability or control. By unifying SimCorp One on Microsoft Azure and leveraging Microsoft Foundry, the company is helping portfolio managers, risk analysts and operations teams access insights faster, automate manual processes and spend more time on higher-value decisions. By standardizing how AI is deployed and governed across global investment operations, SimCorp is creating a foundation for trusted AI at scale, helping organizations embed AI into investment workflows while maintaining the controls required in highly regulated markets. One study found SimCorp One customers realized 134% ROI over three years, improved operational efficiency by 45 percent, saved 10 hours per person per week and accelerated time to market by 50 to 60 days.

Stellantis is accelerating AI-led strategy and digital transformation across its global business, co-developing more than 100 AI initiatives across sales, customer care, product development and operations. By applying AI-powered insights across the business, the automaker is streamlining product development and validation, advancing predictive maintenance and bringing new digital features and services to market faster for customers. Stellantis is also deploying an AI-driven global cyberdefense center to help protect vehicles, customers and operations worldwide while modernizing its infrastructure on Microsoft Azure with a targeted 60% reduction in its datacenter footprint by 2029, powering a more scalable and interconnected digital ecosystem for future digital and connected services and resilient operations.

Facing a growing volume of cyberthreats across a complex healthcare environment, St. Luke’s University Health Network is using Microsoft Security Copilot to help protect the systems clinicians and patients depend on every day. Across 15 campuses, 300 outpatient sites and more than 2.5 petabytes of data and patient records, the organization needed a unified view of threats across a complex environment. Security Copilot connects Microsoft Defender, Microsoft Sentinel, Microsoft Entra, Microsoft Purview and other security tools, helping analysts correlate threats faster, eliminate silos and respond with greater precision. St. Luke’s is saving nearly 200 hours each month in phishing alert triage and creating incident reports in minutes instead of hours, helping security teams focus more time on protecting patient care.

University of Kentucky unified more than 150 AI initiatives across classrooms, research labs, healthcare settings and administrative offices through its CATS AI governance framework. Standardizing on Microsoft’s AI portfolio — including Microsoft 365 Copilot, Microsoft Dragon Copilot, GitHub Copilot and Microsoft Azure — the university achieved campus-wide deployment, providing more than 70,000 students and employees with access to AI capabilities. Clinicians are using Dragon Copilot to reduce documentation burdens and spend more time with patients, and students are using GitHub Copilot to become active builders of digital solutions, such as the Socratic Tutor: an AI-powered learning platform aimed at helping medical students master complex curriculum.

Looking back on FY26, I am inspired not only by the continued pace of AI innovation, but by what our customers are achieving. Across every industry and segment, organizations are turning their unique IQ into strategic advantage with continuously improving agentic workflows. The companies leading this next phase are building, observing and tuning agentic workflows against business outcomes. Intelligence compounds, trust scales and AI can work in the flow of human ambition. As Frontier Firms redefine what is possible with AI, we remain focused on helping our customers amplify and protect their intelligence so they can transform how they operate, compete and grow.

Judson Althoff is the chief executive officer of the commercial business at Microsoft. He is responsible for the product strategy, sales, services, support, marketing, operations and revenue growth of the company’s commercial business, which operates in more than 120 regional and national subsidiaries globally.

The post Looking back on Microsoft’s FY26: From AI experimentation to Frontier Transformation appeared first on The Official Microsoft Blog.

Editor’s note: Updates with additional details on the model’s crash score.

The physics of cybersecurity are changing. Autonomous systems can now reason, adapt and operate continuously. At the same time, the cost of offense is falling, while the volume, velocity and complexity of what must be secured continues to grow. Attackers can generate exploits faster, scale campaigns further and operate with unprecedented efficiency. The approaches built for a world of human actors cannot keep pace with a world of AI, agents and machine-speed attacks.

Security needs a new cyber stack. A new cyber stack must continuously perceive risk across the entire digital estate, reason across vast amounts of context and take action at machine speed. It must learn and adapt as environments evolve, helping organizations stay ahead of threats. And because security is ultimately a human mission, it must amplify defenders with better insights and more powerful ways to act. The defining characteristic of the next generation of security systems will not be their ability to generate more alerts. It will be their ability to continuously perceive, reason and act.

That vision led us to build Project Perception. A new agentic security system designed for the realities of AI. It turns signals into real-time protections using AI to defend against AI.

Project Perception brings together signals, context, models and specialized agents into a continuously learning system of defense. It can reason, prioritize and act at machine speed while keeping humans firmly in control and empowering them with powerful new workflows.

Project Perception is based on a simple idea: effective defense requires continuous understanding of how an attacker sees the world, how a defender evaluates risk and how protections are improved over time. To accomplish this, Perception coordinates three classes of specialized agents. Red team agents identify potential paths to compromise before an attacker can exploit them. Blue team agents investigate, reason over context and determine what represents meaningful risk. Green team agents take corrective actions and strengthen defenses across the environment. Working together, these agents form a closed-loop system that continuously discovers, evaluates and improves an organization’s security posture.

A system like Project Perception is only as effective as the visibility it has, the actions it can take, the experience of the teams building it and the models it can use. Microsoft brings together all four.

We see across identities, endpoints, applications, data, clouds and AI systems, providing broad visibility across the digital estate. Equally important, we can help customers take action across those environments. Combined with decades of security research, threat intelligence and real-world operational experience defending organizations, these capabilities shape how Project Perception reasons, prioritizes and responds.

Security is a 24/7 mission. Organizations need protection that is highly effective, continuously available and affordable at scale. That requires more than access to the most capable model. It requires applying the right model to the right task. Project Perception adopts a multi-model architecture that combines frontier and specialized cyber models, optimizing for both quality and cost.

As part of this multi-model strategy, we are committed to bringing customers the best models for each security task, including innovating with our own specialized models. The first scenario is software vulnerability management, bringing MAI-Cyber-1-Flash inside MDASH, our software vulnerability multi-model team of agents. MDASH with MAI-Cyber-1-Flash delivers 96% (any-crash score*) on CyberGym, an industry benchmark. It also outperforms competitors like Mythos on the CyberGym leaderboard. And this same configuration delivers almost 50% of cost savings vs. the current MDASH configuration in market today. That’s the power of a well-tuned, multi-model system with access to uniquely rich historical training data. Next, Project Perception will take advantage of MAI-Cyber-1-Flash for many more security workflows, beyond the software vulnerability scenario.

We are bringing this vision to customers around the world through Project Perception, which enters public preview on August 3.

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Click here to load media A cyber stack built for agentic security

Delivering agentic security requires more than adding agents to existing workflows. It requires a new cyber stack, designed from the ground up.

The stack begins with signals and sensors that provide awareness across the digital estate. Security context transforms those signals into token-efficient understanding that agents can use. Models provide intelligence and reasoning. A harness coordinates models and agents across security workflows. Agents apply that intelligence across security workflows and actuators translate decisions into protection. Together, these layers create a continuous learning system that can understand risk, adapt to changing conditions and improve security outcomes over time.

While each layer provides important capabilities, the power of Project Perception comes from how they work together.

Effective reasoning requires more than raw signals. Agents need context.

Microsoft transforms its breadth of visibility, threat intelligence and security expertise into a security context that connects security data, knowledge and semantics across the digital estate. The result is a continuously updated representation of an organization’s assets, identities, relationships, risks and activities that gives agents a shared, near real-time, understanding of the environment they are helping to defend.

This shared understanding is foundational to how Project Perception operates. Rather than forcing agents to continuously gather, correlate and reconstruct context from raw signals, it provides them with immediate and token-efficient access to the information they need to reason over risk, prioritize actions and make decisions. By grounding every interaction in this rich security context, Project Perception improves the accuracy and consistency of reasoning while reducing the time, compute and cost required to operate at scale.

No single model will be optimal for every security task. Effective cyber defense requires applying the right model to the right problem at the right time.

For Project Perception, the right model is determined by the combination of quality, reliability, latency and cost. Rather than relying on a single model, Project Perception adopts a multi-model architecture that continuously selects the capabilities best suited to the task, optimizing for both effectiveness and economics. Because security is an always-on mission, sustainable economics are essential to operating protection at scale.

This approach is shaped by ongoing research, benchmarking and evaluation across frontier and specialized models. Our security researchers continuously assess models against real-world security workflows, enabling us to match each task with the model that delivers the best outcome. This allows customers to benefit from advances in AI without being tied to any single model.

Security teams do not need more information. They need better outcomes.

That is why actuators are a critical part of the cyber stack. Project Perception is deeply integrated across Microsoft Security products, enabling agents to connect insights to actions. Organizations can continuously reduce risk rather than simply identify it, helping defenders strengthen security while remaining in control.

Underpinning every layer of the cyber stack is a foundation of trust. Project Perception is built in alignment with Microsoft’s Responsible AI principles and inherits the security, compliance, governance and operational controls our customers already rely on. This ensures these capabilities are delivered with the same rigor, accountability and enterprise readiness that customers expect.

Security has always been a race between attackers and defenders. AI changes the speed, scale and economics of that race. Defenders need systems that can continuously perceive, reason and act alongside them.

 Project Perception is how we begin to build that future.

To learn more about Microsoft Security solutions, visit our website. Bookmark the Security blog to keep up with our expert coverage on security matters. Also, follow us on LinkedIn (Microsoft Security) and X (@MSFTSecurity) for the latest news and updates on cybersecurity.

Other resources:

• Enhancing AI Security Through Global AI Red Teaming 

• From research to reality: An interview with Microsoft VP of Security Research Taesoo Kim

Hayete Gallot leads Microsoft’s work to help organizations operate securely in an AI-driven world. Her scope includes identity, threat protection, compliance and data security at global scale.

Note: *Any-crash measures the ability of the agent to identify vulnerabilities that can crash the code under evaluation with an input that triggers any existing or 0-day vulnerability. Our 96% score is an any-crash score.

The post Rethinking security for the age of AI appeared first on The Official Microsoft Blog.

Today, we’re excited to share a long-term commitment to the Department of Energy’s (DOE) Genesis Mission, backed by a $60 million investment designed to accelerate AI for science and the breakthroughs it can deliver for the country. This deepened commitment includes Microsoft’s new Scientific Partnership Advancing Research & Knowledge coordination hub and program office, otherwise known as SPARK, that is focused on facilitating collaboration and scientific discovery for the Genesis Mission.

The Genesis Mission represents an ambitious vision, bringing together DOE’s 17 National Laboratories, world-class experimental facilities, decades of irreplaceable scientific data and next-generation computing into a single, unified platform capable of transforming how science gets done. The critical goal of the Genesis Mission is to double the productivity and impact of American research and innovation within a decade by embedding AI directly into the scientific process. It’s a bold step forward and exactly the kind of moonshot that defines American science.

At Microsoft, we believe that this goal is not only achievable, but also essential as a national security imperative and an economic engine for generations to come. With this commitment and the launch of SPARK, we’re ready to partner fully in this mission and build on our collaborations across government and with academia.

Today, Microsoft’s $60 million investment package will begin to accelerate AI for science in support of DOE and the National Laboratories. As sustained scientific impact requires more than infrastructure alone to enable lasting scientific outcomes, Microsoft’s investment package is structured in two parts:

• $40 million in Azure compute and AI credits, distributed over three years, to power the large-scale AI and scientific workloads at the heart of the mission. This gives researchers room to train, simulate and iterate at a scale that matches their ambition.
• $20 million in solution engineering enablement services. Dedicated engineering, architecture, deployment, adoption and acceleration support to turn cloud and AI capacity into operational scientific outcomes. This investment helps ensure that the Genesis Mission advances in practice and in principle.

Together, this investment reflects a simple conviction: While infrastructure opens the door, it is people, expertise and disciplined delivery that carry discoveries across the finish line. Microsoft is committing to both.

Great partnerships need more than good intentions and good technology — they need a clear way to work together. So, alongside this commitment, we are standing up a new program office and coordination hub: SPARK, or Scientific Partnership Advancing Research & Knowledge.

SPARK is the single front door and coordination hub for Genesis Mission collaboration with Microsoft. SPARK orchestrates the full breadth of what Microsoft brings to bear: program, technical, research, engineering, security, compliance, partner and field teams into one clear, consistent path from first idea to deployed science.

SPARK is built around five commitments:

• A dedicated Genesis Mission Program Management Office. This office handles intake, prioritization, a disciplined sprint-and-checkpoint cadence and a steady operational interface with DOE for alignment, reporting and sustained partnership.
• An AI for Science Center of Excellence. An integrated delivery team that moves use cases from concept to secure, compliant, scalable implementation, with enablement through office hours, hackathons and training grounded in responsible AI and reproducible research.
• Optimization of Azure credits. Ensures computing resources are directed toward the projects where they can accelerate science most.
• Management of technical services. Dedicated support to help labs adopt and operationalize AI-for-science capabilities, delivered by Microsoft teams and select partners.
• Joint research and development. A focused set of Genesis Mission-aligned challenge problems, co-developed by Microsoft researchers and DOE scientific leadership, with milestones, publications and IP governed by mutually agreed terms.

Underneath SPARK sits the full breadth of Microsoft’s secure, FedRAMP-authorized cloud and AI portfolio. This includes Microsoft Azure infrastructure, Microsoft Foundry and Zero Trust security through Microsoft Defender, Sentinel and Entra. Azure capabilities will help extend the DOE’s American Science Cloud capabilities by providing computing, AI, data and collaboration services that complement existing scientific infrastructure. Together, these capabilities will help accelerate discovery, enable secure collaboration across institutions and provide researchers with flexible access to advanced technologies and resources. This will enable DOE and National Laboratory scientists to move faster from hypothesis to discovery, without compromising the security, reproducibility and governance that mission-critical research demands.

Microsoft’s new commitments build on capabilities we recently announced through Microsoft Discovery and Microsoft Quantum. They are designed to accelerate scientific research and will support the Genesis Mission’s ambition to bring AI, advanced computing and emerging technologies more deeply into the scientific process.

To enable a unified research platform for Genesis Mission work, Microsoft will provide access to Microsoft Discovery, our integrated platform that unites AI models, simulation, data and experimental workflows driven by advanced cognition rooted in scientific method, within a single governed environment. Microsoft Discovery, now generally available, includes support for autonomous lab orchestration, integration of Microsoft Research’s AI models for science, continuous AI learning through the scientific loop and agentic memory, advances in Discovery Bookshelf for data curation and scalable indexing and deeper integration and multi-hop reasoning over enterprise science data estates, including data governance. Together, these capabilities are designed to help researchers move more quickly from data to insight, from simulation to experiment, and from promising idea to scientific breakthrough.

We also recently announced the preview of the Microsoft Discovery app, a local desktop experience that helps researchers, students and scientific teams begin working with Microsoft Discovery today. Our support for the Genesis Mission will include access to the Microsoft Discovery app to enable DOE and National Laboratory teams with early access to its capabilities.

Microsoft’s recent quantum progress further strengthens the foundation for Genesis Mission work. With Majorana-based quantum advances, including more reliable topological qubits and a roadmap toward scalable quantum computing, Microsoft is helping move quantum from long-range research toward practical scientific capability. For DOE and National Laboratory teams, that progress can open new ways to model complex materials, chemistry, energy systems and national security challenges that are difficult or impossible to solve with classical computing alone. Just as important, the same agentic AI for science approach behind Microsoft Discovery is helping accelerate quantum research itself, creating a reinforcing cycle in which AI speeds quantum breakthroughs and quantum computing expands what scientists can ultimately discover.

Working alongside and supporting federal researchers and universities, we’re already seeing real-world impact, from faster materials screening to advanced biosurveillance modeling. Four of the first projects taking shape under this partnership show what that looks like in practice:

Discovering critical energy storage materials and biosystems design (Pacific Northwest National Laboratory)
Using AI to dramatically accelerate the discovery of new energy storage materials through novel experimentation design and shrinking analysis of massive volumes of scientific data from years to weeks. In biosystems design, Microsoft Discovery is plugging directly into PNNL’s laboratory automation infrastructure to launch self-driving scientific workflows that autonomously design, run and fine-tune biological experiments in real time.

Strengthening biosecurity (Lawrence Livermore National Laboratory)
Pairing advanced AI with modern bioinformatics to identify emerging biological threats sooner and develop responses faster, strengthening the nation’s ability to stay ahead of the next outbreak and developing proactive defenses against evolving biosecurity risks. By applying advances in AI, scientific computing and biotechnology, Microsoft and the Lawrence Livermore National Laboratory are helping strengthen the nation’s ability to prevent, detect and respond to biological threats, advancing US biosecurity and biodefense priorities while supporting the long-term resilience and competitiveness of the US bioeconomy.

Autonomous labs for accelerated materials discovery (Johns Hopkins University Applied Physics Laboratory)
Supporting the development of AI-enabled autonomous (“self-driving”) laboratories through the integration of advanced foundation models and simulation tools — including MatterGen and MatterSim — to rapidly design, execute and optimize experiments for structural materials and superconductors. By combining autonomous experimentation with accelerated computational materials discovery, this approach dramatically shortens the path from scientific hypothesis to validated, manufacturable, mission-enabling materials, while exploring a vastly larger design space than is possible through traditional methods.

Accelerating nuclear energy permitting and autonomous energy operations (Idaho National Laboratory)
Applying AI to streamline the licensing, permitting and review of new nuclear projects. AI helps automate the development of complex engineering and safety analysis materials, a historically time-consuming and expensive process, to get this critical documentation into the hands of regulators and safety reviewers sooner. The lab also demonstrated that secure, distributed, hyperscale cloud can enable autonomous remote operations for nuclear power generators. Together, these efforts are helping bring safe and reliable power online faster and ensure scalable, efficient and reliable operations to meet the nation’s surging demand for energy.

We are entering an era where AI and quantum don’t just support the scientific process — they are essential to it. Through the Genesis Mission, Microsoft is committing to work as part of America’s research enterprise by providing hyperscale compute, advanced models, emerging quantum capabilities and dedicated expertise that run alongside the labs’ own world-leading systems. We are helping to ensure that the nation’s scientific strength is built on American technology. This commitment builds and expands upon the decades of collaboration through Microsoft Research and partnerships across the US government and our national labs. Now is the time to scale these breakthroughs across the entire R&D ecosystem.

The mission ahead is significant, and it should be. Whether it’s advancing the frontiers of energy, chemistry and materials science, hardening our national security or simply giving the country’s best scientists better tools to ask bigger questions, this is work worth doing and worth doing together. With a $60 million investment and SPARK, Microsoft is ready to help ensure America’s scientific leadership continues to accelerate.

Fueling scientific discovery is one of the most important things these technologies can do. AI and quantum are transforming how we tackle the toughest challenges facing the country, from sustainable materials and clean energy to biosecurity and drug discovery. The Genesis Mission is a pivotal step toward advancing that progress. We commend the DOE’s vision in building a unified, holistic approach to these critical national missions. For America to seize this opportunity and set the global standard, we must put these breakthrough technologies to work advancing the nation’s research and development.

We’re ready to deepen this partnership across government and academia. We move faster together. I’ve never been more optimistic about what’s possible when the nation’s scientific talent meets the best of modern AI and quantum. The Genesis Mission is that moment. Let’s get to work.

To get started, reach out to your Microsoft account team or email genesismission@microsoft.com.

Chris Barry leads Microsoft’s US Public Sector business. He partners with federal, state and local government and higher education agencies and organizations to accelerate digital transformation, bringing Microsoft’s secure cloud and AI capabilities to mission-critical work that serves communities across the nation.

The post Powering America’s Genesis Mission: Microsoft’s commitment to scientific discovery appeared first on The Official Microsoft Blog.

AI workloads are scaling faster than any single infrastructure approach can support — with more models, new agent-driven workloads and surging compute demand driving the need for greater specialization across the stack. To meet this need, Microsoft continues to evolve Azure’s infrastructure, including expanding its AI fleet with AMD’s most advanced AI and high-performance computing (HPC) solutions.

Our approach to AI infrastructure is designed to support the breadth of how AI systems are built and run. We closely work with industry innovators like AMD as well as our own purpose-built silicon and systems to provide customers with a comprehensive, open and heterogenous platform to achieve the best performance, cost and energy efficiency outcomes.

Building on our close collaboration with AMD, Microsoft is bringing AMD’s latest Helios AI platform and next-generation EPYC datacenter processors to Azure. These technologies will power three upcoming Azure offerings: HDv2 VMs for data processing, HXv2 VMs for electronic design automation (EDA) and ND MI455X v7 VMs for AI inference workloads.

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CPU infrastructure is essential to the performance and efficiency of modern AI systems. AI accelerators depend on high-density, power-efficient CPU compute to process data, coordinate workloads and keep pipelines running at scale. Without this, training jobs don’t have enough data to learn from, and agents don’t have enough capacity to perform tasks on behalf of customers. Azure HDv2 virtual machines are one of our latest offerings designed from the ground up to eliminate these bottlenecks and empower massive agentic workload adoption.

Co-designed with AMD, HDv2 VMs expand Azure’s portfolio of purpose-built solutions for the most demanding CPU workloads from AI customers, including data preparation, search, reinforcement learning and agent coordination at scale. Featuring nearly 500 physical 6th Gen AMD EPYC CPU cores, 4 terabytes of RAM, 32 terabytes of local NVMe storage and 400 Gb Azure Boost networking, HDv2 VMs are built for the workload needs of our most demanding AI customers.

The AI era has created tremendous need and opportunity for firms developing the silicon products that power this infrastructure. For this reason, Azure HX virtual machines, launched in partnership with AMD in 2023 and featuring AMD’s unique 3D V-cache technology, have seen significant adoption among silicon design firms working to bring more capable and efficient AI silicon to market. Today, we are announcing the next step in our workload optimized journey for these customers, HXv2.

HXv2 virtual machines build on and extend the strengths of HX. They both continue the differentiation Azure offers for RTL simulation workloads by again employing 3D V-cache technology, while offering significant improvements to single threaded performance and memory. HXv2 VMs will feature 176 AMD 6th Gen EPYC CPU cores with a clock frequency of more than 5 GHz, 50% more addressable cache per core and VM sizes with nearly 2 or 4 terabytes of RAM, helping customers optimize their workloads to memory needs.

Azure HXv2 is also designed to support a broader range of technical computing workloads including scientific simulation, engineering analysis and other distributed memory applications. The significantly increased per VM and per core performance, and the inclusion of 800 Gb InfiniBand, enable large-scale MPI-based simulations and make HXv2 an ideal fit for a wide variety of HPC customers.

AMD, a leading HX-series customer, highlights this impact directly:

“Engineering teams are pushing the limits of simulation, chip design and scientific computing. At AMD, we experience those demands firsthand as we design future AMD EPYC CPUs and AMD Instinct GPUs. Azure HX is an important platform for scaling complex EDA workloads, and we’re excited about Azure HXv2, which is designed to deliver even greater performance and scalability. We look forward to continuing our collaboration with Microsoft as we help advance infrastructure for the world’s most demanding engineering and scientific workloads.”

— Mark Papermaster, Executive Vice President and CTO, AMD

The HXv2 also leverages Microsoft’s long-standing collaboration to optimize Synopsys AI-powered EDA solutions on Azure:

“As AI compute continues to push the limits of semiconductor design, our collaboration with Microsoft on the Azure HX-series demonstrates a shared vision for enabling customers to deliver next-generation AI systems with precision and scale in accelerated design cycles. These systems have enabled Synopsys customers to reliably and efficiently leverage cloud-based compute, extending EDA workloads beyond traditional infrastructure constraints so they can meet ambitious development schedules while maximizing design quality and delivering dramatic performance gains.”

— Shankar Krishnamoorthy, Chief Product Development Officer, Synopsys

ND MI455X v7 is designed for the reasoning, search and agentic workloads behind modern AI services. Powered by the AMD Helios rackscale solution, it expands Azure’s infrastructure options for large-scale inference and is designed to deliver strong performance and efficiency for demanding AI workloads.

Together, these new capabilities expand Azure capabilities while giving customers more flexibility to choose the right compute for each unique AI workflow: from inference, to data systems, to chip design. Customer choice is a core design principle built directly into Microsoft Azure, and we’re excited to bring AMD’s most advanced innovations at production scale.

To learn more about Azure’s high-performance computing and AI infrastructure capabilities, visit Azure.com.

Scott Guthrie is responsible for a set of hyperscale cloud computing solutions and services including Azure, Microsoft’s cloud computing platform, generative AI solutions, data platforms and information and cybersecurity. These platforms and services help organizations across the globe solve urgent challenges — and transform for the future.

The post Microsoft expands Azure AI and HPC infrastructure with AMD appeared first on The Official Microsoft Blog.

Amy Coleman, EVP and Chief People Officer, shared the following communication with employees today.

When I stepped into this role, I promised to communicate more openly with you and share the “why” behind our decisions.

Today we are eliminating around 4,800 roles, about 2.1% of our global workforce, as we focus our people, investments, and energy on the priorities that will keep Microsoft positioned to deliver for customers in a fast-changing industry. The people whose jobs are impacted today are our colleagues and friends. They have made meaningful contributions to Microsoft, and we are deeply grateful for everything they have done.

Decisions like these are never easy, and you have my commitment that we are always looking for ways to reduce the need for job eliminations. Whenever possible, our priority is to place people into new roles aligned to the company’s highest priorities and greatest areas of opportunity. Over the past year, we have redeployed more than 4,000 employees into new roles, including another 500 this month. We will also transition four of our gaming studios to operate under new management, with the goal of preserving both their intellectual property and ongoing projects. In addition, more than 30% of eligible employees chose to participate in our recent voluntary retirement program, and we will continue exploring similar approaches in the future. While this doesn’t change the difficulty of today’s news, we will continue to do everything we can to create opportunities for our people, reduce the need for job eliminations where possible, and responsibly support those affected with care and respect.

The “why” is this: Our business is changing because the world around it is changing. The way technology is built, deployed, and used is transforming faster than at any point in my time here. Our customers’ needs are shifting, the business models that serve them are shifting, and that means the work itself – what we do, where we focus, and how we’re organized – has to transform too. Companies don’t get to choose whether their industry changes; they only get to choose whether they change with it. That means we will need to adjust resources and roles and shift how we operate so we can have the greatest impact for our customers.

I also want to be direct that the roles eliminated today are not being replaced by AI. At the same time, what is true is that AI is changing how work gets done. Some of the tasks we do every day can now be automated, and that means we all need to keep learning, keep building new skills, and keep adapting as the work evolves. Our customers are navigating this same shift, and they’re counting on us to help them through it. We can’t do that well unless we’re doing it ourselves. This comes down to two commitments: making the decisions needed to drive differentiated customer value, and supporting the people affected by them.

First, we will make the hard changes required to build differentiated products and services that deliver differentiated customer value. We are aligning our investment, people, and energy to our business priorities. Today’s changes mostly fall within our Commercial and XBOX organizations. In our Microsoft Commercial Business, they build on last week’s Frontier Company announcement, reshaping how we work and embedding our engineering experts alongside customers so we can help them accelerate their technology deployments. In XBOX, we are restructuring to position the business for long-term success. Engineering teams across the company will also evolve their structure and priorities to meet customer needs and innovate for the future.

Second, we will do this thoughtfully.  As mentioned above, we are working on alternative solutions to job eliminations, and beyond this, we will continue to invest in equipping employees with new skills, including in AI. For those who are impacted, we provide financial support and resources to help them take their next step.

I know many of you want to help those who are leaving but aren’t sure how. Reach out and check in on your colleagues. Use your network to bring people together, share what makes them exceptional, and help create connections to opportunities that might not happen otherwise.

We are still early on this journey, and there will be more changes ahead; other parts of our business will need to make similar changes. Each time, you can hold us to the two commitments.

During my time at Microsoft, I’ve seen this company reinvent itself again and again. What makes that possible has always been our people – their resilience, creativity, and willingness to keep learning.

Thank you for everything you bring to Microsoft.

Amy

Read more: Resetting XBOX.

The post The latest in our company transformation appeared first on The Official Microsoft Blog.

The pace of AI adoption is moving incredibly fast. Customers have moved well beyond experimentation and understand the importance of adopting AI to transform their business. They are now concentrating on delivering measurable business outcomes and demonstrating a return on their AI investments, while ensuring their intelligence is amplified and their IP is protected.

Today we are introducing Microsoft Frontier Company, a new operating business focused on delivering Frontier Transformation through AI for our customers around the world. It will provide a unique combination of skills inclusive of deep industry knowledge, change management and continuous improvement experience, and enterprise-grade AI engineering expertise. This goes beyond what has been labeled as Forward Deployed Engineering (FDE) and will be the largest, most capable, outcome-driven engineering organization in the industry. We are making a $2.5B investment in Microsoft Frontier Company, embedding 6,000 industry and engineering experts at customers to co-design, co-innovate, deploy and continuously improve AI systems at scale based on measurable business outcomes.

I recently wrote more about my conviction that Intelligence + Trust are the two most important components of any AI solution and how our customers can use different levers to manage cost.

Companies need to establish an intelligence platform so their unique IQ — their proprietary data, expertise, workflows and decision-making processes — compounds over time from within, using their choice of models to build AI solutions and workflows.

They need a trusted platform that allows them to observe, govern, manage and secure AI solutions across every layer of the technology stack, using FinOps to assess their ROI.

Enterprise AI engineering expertise with deep industry knowledge is required to build a system that acts as a continuous loop of improvement between the two platforms to fine tune agentic business processes, ensuring that a customer’s intelligence compounds over time and delivers real business outcomes.

This is what Microsoft Frontier Company was built to do: focus on end-to-end Frontier Transformation, enabling customers to amplify their IQ with AI while refining their differentiated value in the markets that they serve.

Early results demonstrate meaningful impact: Our engineers and industry experts partnered with LSEG (London Stock Exchange Group) to embed AI into LSEG Workspace, helping finance professionals ask complex questions and get quick answers across structured and unstructured financial content. The solution is underpinned by a foundation that is iteratively refined through client feedback and real-time user testing that accelerates each cycle and steadily improves model quality and scope. From LSEG to Land O’Lakes to Unilever to Novo Nordisk, our differentiated approach is already delivering measurable outcomes on our customers’ Frontier Transformation journeys.

To achieve scale, we will work closely with our partner ecosystem to extend this unique value to our customers across all markets and segments globally. We have robust FDE partnerships with our Global SI partners, including Accenture, Capgemini, EY, KPMG, PwC and others.

Central to this approach is a principle that is non-negotiable: a customer’s IQ is protected. Their data, their IP, their competitive advantage — none of it is used to train models in ways that commoditize what differentiates them in their industry. Satya put it clearly recently: there is no societal permission for an AI future that eats the intelligence of the companies it’s deployed inside. We built Microsoft Frontier Company to make sure that does not happen.

We protect that intelligence with a model-diverse, open, heterogeneous AI platform. Customers shouldn’t be locked into a single model any more than they should be locked into a single technology vendor. Microsoft’s platform gives organizations the flexibility to run the right model for each scenario — whether it comes from OpenAI, Anthropic, Microsoft AI, open source or a specialized model tuned for a specific industry — without ceding control to any one of them.

To lead this new organization, I have asked Rodrigo Kede Lima to be the President of Microsoft Frontier Company. Rodrigo brings 30 years of industry experience, and for the past six at Microsoft has led enterprise-wide transformations as a sales leader in the Americas and Asia. He has been at the forefront of helping customers and partners translate technology shifts into business outcomes, and understanding how platform innovation, engineering and partner ecosystem collaboration come together to drive growth.

I am excited about all the things that Microsoft Frontier Company will do for our customers to realize the gains of Frontier Transformation. At the end of the day, it comes down to Intelligence + Trust and empowering our customers to achieve meaningful outcomes and a return on their investments.

Learn more at www.microsoft.com/en-us/frontier-company.

Judson Althoff is the chief executive officer of Microsoft Commercial Business. He is responsible for the product strategy, sales, services, support, marketing, operations and revenue growth of the company’s commercial business, which operates in more than 120 regional and national subsidiaries globally.

The post Microsoft Frontier Company: AI engineering that amplifies and protects your intelligence appeared first on The Official Microsoft Blog.

As demand for cloud and AI services continues to grow, datacenters are becoming more essential than ever. Communities also want to better understand how this infrastructure affects local resources, particularly water. At Microsoft, water stewardship has been a priority since our first datacenter builds in the early 2000s and remains a core part of our strategy today and into the future. It underpins both our Community-First AI Infrastructure initiative and our company-wide commitment to become water positive by 2030, meaning we will replenish more water than we withdraw. We are pairing our progress with continued transparency so people can understand not only how much water we use, but also how we are working to reduce that use over time.

Through continuous innovation and advancements in cooling technologies, we have improved our water use effectiveness (WUE), measured in liters per kilowatt-hour (L/kWh), by nearly 90% since our first generation of datacenters in the early 2000s. Our average WUE has decreased from 2.3 L/kWh to 0.27 L/kWh in 2025, reflecting decades of innovation and our ongoing commitment to reducing the water intensity of our datacenters while meeting the growing demand for cloud and AI services.

Across our entire owned fleet of datacenters, we are committed as a company to a 40 percent improvement in datacenter water-use intensity by 2030. As of 2025, we have reduced our water-use intensity by 25 percent, putting us well over halfway toward our goal. This strong progress reflects the impact of our continued investments in water-efficient cooling technologies, operational improvements and responsible water management practices.*

In FY25, Microsoft reached an important milestone toward its 2030 water positive commitment, replenishing more water than it withdrew across its global operations for the year. We have made this progress by decoupling datacenter growth from water use through resilient, responsible water stewardship practices and the deployment of increasingly efficient cooling technologies — demonstrating that digital growth and sustainable water management can advance together. We are committed to building on this progress and working toward sustaining water positive performance over time as we continue advancing toward our 2030 goal.

Beginning with some of our earliest datacenter designs, we prioritized water efficiency while minimizing impacts on energy use through the deployment of high-efficiency economizing chillers operating at elevated water temperatures. As early as 2008, we adopted direct air cooling with evaporative assist as the primary cooling approach across our datacenter fleet. This design uses significantly less electricity and up to 90% less water than traditional water-based cooling systems by relying on water only when outside temperatures exceed 85°F (29.4°C). In parts of Northern Europe, no water is required for cooling throughout the year, while in other cooler regions like Dublin and Amsterdam, water is used less than 5% of the time. In more temperate climates like Virginia, water is typically required only 10% of the year, while in the hottest climates like Phoenix, water use may increase to as much as 40% of the year. As a result, approximately 90% of our 2025 owned fleet operates using highly efficient, low- to zero-water cooling systems.

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While the majority of our existing datacenters are already highly water efficient, we did not stop there. In 2024, Microsoft introduced a new datacenter design optimized for AI workloads that consumes zero water for cooling during operations, further reinforcing our commitment to water stewardship by design. This chip-level cooling solution delivers precise zonal temperature control without water evaporation by recirculating water through a closed-loop, direct-to-chip cooling system. As our datacenter fleet continues to expand, the addition of these zero-water designs will further reduce Microsoft’s water use intensity over time.

Datacenter cooling methods, explained:

• Cooling towers: Traditional systems that remove heat by evaporating water year-round.
• Hybrid fluid coolers: Evaporates water for cooling during hot summer conditions and switches to dry mode when ambient temperatures cool down.
• Direct air: Uses outside air for cooling, with little to no water use. Water is used only when outside air is above 85°F.
• Air cooled chillers: Uses mechanical refrigeration and outside air to remove heat from closed coolant loops with zero water evaporation.
• Liquid-cooled AI DCs: Uses closed-loop, direct-to-chip cooling to provide precise chip-level temperature control, removing heat efficiently with zero water evaporation.

Design innovation is only part of the story. We are also improving the efficiency of existing facilities that use water through a continuous focus on optimizing temperature and humidity setpoints, enabling more precise environmental control and eliminating overcooling. In addition, we regularly audit water use and compare actuals against design expectations using real-time weather data and operational analytics. This helps ensure our datacenters are performing as intended and enables us to quickly identify and address any unexpected water use. These efforts, combined with ongoing hardware and operational improvements, are all aimed at using as little water as possible.

Specifically in our Phoenix, Arizona, datacenters, implementation of these advancements led to a 23% year-over-year improvement in WUE in FY25 alone. We are now deploying these advancements across our direct-evaporatively cooled datacenters globally.

Operational improvements like these are one reason Microsoft has been able to report significant long-term reductions in water intensity across datacenter generations. They also point to the next phase of our work: expanding the use of recycled and alternative water sources wherever possible.

In addition to driving efficiency, we also prioritize using recycled, reused or non-potable water wherever possible in our operations. We have expanded the use of these non-potable water sources in some of our most water-intensive regions, helping reduce demand on freshwater supplies. For example, in Quincy, Washington, Singapore and San Antonio, Texas, three of our key locations for advancing water stewardship, we leverage 74%, 99% and 79% recycled, reused or non-potable water sources, respectively.

Rainwater harvesting systems are now operational at select datacenters in the Netherlands, Sweden and Ireland, with additional installations planned in Canada, the United Kingdom, Finland, Italy, South Africa and Austria. To illustrate the potential impact, Microsoft’s new datacenters in Quebec are expected to collect up to 1.5 million liters of rainwater annually, depending on local precipitation levels. This water can be used to further offset the already low water withdrawal at these sites. Expanding the use of alternative water sources in this way helps reduce pressure on municipal water supplies while supporting efficient datacenter operations.

As needed, we implement on-site water treatment systems that enable facilities to recycle water multiple times for cooling operations. These systems produce purified water suitable for reuse within cooling systems, reducing overall dependence on utility water supplies. Together, these efforts demonstrate how engineering innovation and operational excellence can work in concert to meaningfully reduce water use at the facility level.

We work closely with local utilities to reduce strain on community resources, and plan ahead for the sourcing and infrastructure needs associated with our operations. Beyond our operations, Microsoft’s Datacenter Community Pledge commits us to protecting local watersheds, engaging stakeholders and investing in projects that strengthen regional water resilience — helping ensure datacenter growth supports both environmental sustainability and long-term community well-being. Where system improvements are required, Microsoft funds those upgrades in full so communities do not have to shoulder the cost of supporting our operations.

Beyond our own footprint, we invest directly in community water infrastructure by modernizing water systems, expanding access, increasing reliability and helping utilities maintain stable rates and pressure. These investments create shared value for both Microsoft and the local communities we work closely with by strengthening critical infrastructure and supporting long-term water resilience. For example, near our datacenter in Leesburg, Virginia, Microsoft is funding more than $25 million in water and sewer improvements to help ensure that the cost of serving our facilities does not fall on local ratepayers. Since 2020, we have invested more than $500 million in more than 75 water and wastewater infrastructure projects that deliver meaningful community co-benefits.

Finally, we pair all of this work with our broader water positive commitment, our goal to replenish more water than we withdraw by 2030, while advancing our Community-First AI Infrastructure approach, which prioritizes delivering measurable benefits to the communities where we operate. This approach extends across our entire datacenter footprint, including leased facilities. In FY25, we replenished more water than we withdrew globally, marking a significant milestone in our water stewardship journey.**

We prioritize and pursue projects designed to deliver meaningful water contribution to each local community. For example, in the greater Phoenix area and nearby Nevada communities, we partner with FIDO Tech and local utilities to deploy AI-enabled leak-detection that identifies and repairs hidden breaks in aging water systems. By preventing water loss before it occurs, these efforts help keep more water in circulation, improving reliability for residents and effectively increasing the amount of usable water available across the system.

Across the Midwest, we work with The Nature Conservancy to restore historic oxbow wetlands — crescent-shaped water bodies that naturally recharge groundwater, reduce flood risk and enhance habitat for native species. These wetlands act as natural reservoirs, capturing and slowly returning water to local aquifers over time. The goal is to create more stable water availability for agriculture, healthier ecosystems and increased resilience for nearby communities throughout the year.

As water challenges become increasingly complex around the world, Microsoft remains deeply committed to protecting water as a vital natural resource. We continue to advance datacenter innovations that reduce water use intensity while supporting the growing performance demands of cloud and AI services. Through zero-water cooling designs optimized for AI workloads, water reuse initiatives both on and off our campuses and community-focused stewardship programs, we are working toward a future where digital growth and responsible water management go hand in hand.

We are also exploring zonal cooling architectures that more precisely align cooling approaches with the needs of different hardware types, improving efficiency while supporting a diverse mix of AI and traditional workloads.

Datacenters are essential infrastructure for the digital economy, and we believe they should be built and operated in ways that benefit the communities they serve. Over the last decade, we have demonstrated that technological advancement and environmental stewardship can progress together, and we remain committed to continuing that journey as we build the datacenters of the future.

Top image caption: Aerial view of Microsoft datacenter campus in Wisconsin.

Judy Priest leads technology strategy, innovation and research for Microsoft’s global cloud and AI infrastructure, driving advances in datacenter architecture, sustainability, power, cooling, energy and emerging technologies that enable reliable, scalable services.

Steve Solomon is a professional engineer leading the engineering strategy behind the company’s global cloud and AI infrastructure. He specializes in datacenter design, sustainability, power and cooling innovation, helping advance reliable, efficient and community-focused infrastructure at hyperscale.

*Footnote 1: Measured as water withdrawals per megawatt (MW); based on a 2022 baseline.

**Footnote 2: To understand how Microsoft calculates and tracks water replenishment data, please refer to our FY 2024 Environmental Data Fact Sheet.

The post Inside Microsoft’s two-decade push to cut water intensity while scaling for growth appeared first on The Official Microsoft Blog.

Cloud operations are entering a new era as AI-driven and autonomous agents become a larger part of modern software systems. As software becomes increasingly agentic, the challenge is no longer just managing greater scale and complexity. Operators must also contend with systems that evolve faster, act more autonomously and interact across an expanding network of dependencies.

As applications, models, APIs and infrastructure become increasingly interconnected, their behavior is harder to understand end to end. Systems no longer fail in isolation. They fail through interactions across dependencies, services and environments that are constantly changing in real time.

To help organizations operate effectively in these increasingly dynamic environments, today we’re announcing the general availability of the Azure Copilot Observability Agent. Built on Microsoft Azure Monitor, it correlates signals across agents, applications, infrastructure and services to provide the context needed to operate confidently in this new environment.

In a recent survey of 250 IT decision-makers, Microsoft and Material found that 84% of organizations report increased cloud complexity, with 69% saying it is outpacing their current operating model. The impact is most acute across security, cost management and performance, and it extends across the entire operations lifecycle.

As the pace and scale of change accelerate, no individual or team can realistically maintain the full context required to diagnose and resolve issues quickly enough. This is driving a shift toward agentic operations, where intelligence augments how systems are understood and managed.

Observability is foundational to this shift. It provides the real-time understanding of system behavior that agents depend on to reason, adapt and act. Without a connected view across signals, even the most advanced agents lack the context required to operate reliably.

We designed the Observability Agent to help operators move more quickly from detection to understanding. It connects logs, metrics, traces, topology and operational context across environments, reducing the time it takes to identify the root cause of an issue.

As telemetry spreads across systems, operators are often forced to piece together context across multiple tools. The Observability Agent addresses this fragmentation by reasoning across signals in real time and unifying that context into a single operational view. These agentic capabilities are integrated directly into existing workflows, helping teams move from investigation to resolution faster with clear, actionable insight.

We’re already seeing customers use the Observability Agent to reduce manual effort, accelerate incident resolution and improve operational clarity:

“The biggest value is speed! The [Azure Copilot] Observability Agent helps us resolve incidents faster and reduce operational overhead by turning logs, metrics and traces into plain English insights. These agents run deep investigations and provide remediation recommendations almost immediately, compared to hours or even days previously.

Since adopting these capabilities, we’ve reclaimed an estimated 250 engineering hours monthly that are now redirected toward supporting new applications and features. We can use natural language to detect, diagnose and remediate issues faster than ever before.”

— Narmada Krishnaswamy, Head of KPMG Audit Application Support and Operations

“Azure Copilot Observability Agent helped us move from manual incident hunting to faster, AI-guided investigations. For PolicyVault, it pulls together the telemetry from our service, correlates it with Azure resource health and gives us actionable next steps based on the investigation. That means we’re not just seeing what broke; we’re getting a much clearer idea of why it happened and what to do about it, which saves us a lot of time during incidents.”
— Vladimir Gusarov, Founder & CEO, PolicyVault

“Azure Copilot’s Observability Agent helps us move faster from signal to insight. By bringing together our telemetry and guiding us toward likely root causes, it reduces the time and effort needed to investigate incidents and keeps our teams focused on what matters most.”
— Theus Hossmann, Chief Technology Officer at Ontinue

Beyond improving incident response, this shift reflects a new approach to cloud operations, where systems can continuously reason across signals and act on that understanding.

Check out our Tech Community blog post to learn more about the Azure Copilot Observability Agent.

Observability is part of a broader shift to agentic operations. As systems become more autonomous, operations expand from understanding what is happening in production to continuously improving how those systems behave over time.

In an agentic model, this forms a lifecycle. Systems generate signals, agents interpret those signals, take action and learn from outcomes. Over time, this creates a feedback loop where each operational cycle improves the next, increasing system resilience and efficiency.

This shift requires more than better visibility. It requires a coordinated approach across the lifecycle, from observability and diagnosis to optimization and remediation where insight and action are tightly connected.

As agents take on a greater role in that lifecycle, governance becomes central to how systems are trusted and controlled. Policy, auditability and guardrails ensure that actions taken by agents align with organizational intent and operate within defined boundaries. Human oversight remains essential, not as a bottleneck, but as a mechanism for building confidence and ensuring reliability as automation scales.

This is where Azure is uniquely positioned. By bringing together observability, automation and governance within a connected platform, Azure enables organizations to move from isolated tools to an integrated operational model that spans the full lifecycle.

Azure Copilot Observability Agent plays a key role in this model by grounding agentic systems in real-time operational context. As organizations build and deploy more agents, this foundation becomes critical for ensuring those systems operate effectively and responsibly.

Cloud operations are shifting from reactive management to a continuous, agent-driven lifecycle of learning, adaptation and control. This vision of agentic cloud operations is already taking shape across Azure. Read our companion Azure Blog post for more details.

Brendan Burns is a co-founder of the Kubernetes open source project and corporate vice president for Azure cloud-native open source and the Azure management platform including Azure Arc. He is also the author and co-author of several books on Kubernetes and distributed systems.

The post Rethinking cloud operations with agentic observability appeared first on The Official Microsoft Blog.

Today, Microsoft is announcing one of the largest single capacity additions in our history. In Pecos, Texas, we will build a new datacenter campus, expanding our global datacenter capacity by approximately 2 gigawatts (GW) to meet strong and sustained customer demand for AI and cloud services across industries and regions. Beyond the technology, this is a major investment in West Texas. We expect to support over 6,000 construction jobs at peak build-out and to create hundreds of permanent operational jobs that will add a new industry that supports the local economy when the new datacenter campus is operational.

This multibillion-dollar datacenter campus investment over the next five to seven years reflects both the immediate needs we are seeing today and the future trajectory of AI and advanced compute, where reliable infrastructure at scale is essential to unlocking the next generation of innovation. This expansion is grounded in a simple principle: we build where our customers need us, and we build for the long term. We have a track record of doing exactly that in Texas. In the San Antonio region, where we have operated datacenters for nearly a decade, our investment has generated billions of dollars in local economic activity and supported thousands of local jobs. We are committed to delivering the same lasting value in Pecos.

Customer demand for AI and cloud services continues to grow rapidly, from startups building new applications to governments, healthcare providers and educational institutions modernizing critical systems. Meeting this demand requires not only more datacenter capacity, but capacity that is predictable, resilient and able to scale quickly.

The datacenter campus in Pecos enables us to deliver on that need. By pairing new datacenter infrastructure with dedicated energy supply located onsite, we can bring capacity online at the pace our customers require while maintaining operational reliability. Critically, the energy infrastructure required to power this datacenter is being funded by Microsoft. We are paying for the new generation and supporting infrastructure needed to serve our own operations. The capacity we bring online in Pecos is built to meet our demand, ensuring that our growth strengthens, rather than strains, the energy resources the community relies on.

While meeting customer demand is critical, how we grow is equally important. At Microsoft, our Community First approach guides us where we build, own and operate our datacenters, including our new datacenter campus in Pecos.

This work begins with a simple commitment: we show up as a lasting partner, not just a builder of infrastructure.

As shared in our letter to the community in Pecos and Reeves County, we are approaching this project as a new neighbor, with a focus on partnership, transparency and listening. We recognize that earning trust takes time, and we are committed to ongoing engagement with local residents, leaders and organizations as this project moves forward. The region’s elected leadership has welcomed the investment. Reeves County Judge Leo Hung, the county’s top elected official, said:

“We are excited to welcome Microsoft to Pecos. This investment reflects the strength of our region and its ability to support innovation at a global scale. It will create new opportunities for local businesses, support workforce development and reinforce Pecos as a place where forward-looking companies can grow and thrive.”

1. Listening and engaging early
We engage early and often through community meetings, local partnerships and ongoing communication across the life of the project, which gives residents multiple ways to ask questions and share feedback, just as we have in other Texas communities.

2. Creating local economic opportunity
This project is built to drive lasting regional growth. As well as supporting thousands of construction jobs, the hundreds of permanent operational roles will add a new industry to the local economy. We will also invest in workforce development and small-business support. We are focused on ensuring that local residents are prepared to take advantage of the opportunities created by the AI economy. This is part of a sustained commitment to the region, building on more than a decade of experience in Texas, including our operations in San Antonio:

Near San Antonio, where we have operated for nearly a decade, our Datacenter Academy partners with local colleges to prepare students for datacenter careers, including a $545,000 investment that has already reached more than 450 students. Statewide, workforce programs like TechSpark have helped create more than 1,100 jobs and engaged 20,000 Texans in digital skilling. We will bring the same model of local hiring, training and small-business support to West Texas.

3. Partnering for lasting community impact
Our investment reaches well beyond the datacenter, into education, digital inclusion and nonprofit partnerships. In fiscal year 2024, Microsoft and its employees contributed $11 million in cash and $103.3 million in donated software and cloud technology to more than 10,000 Texas nonprofits, alongside 42,000+ employee volunteer hours. In Pecos, we will direct that same commitment toward the priorities that matter most to West Texas residents.

As we expand our datacenter footprint, we remain equally committed to building and operating our infrastructure in ways that reduce environmental impact.

Energy and emissions
This includes improving energy efficiency across our infrastructure, from compute to hardware, and building on the 4.7 GW of renewable electricity we have already contracted for our electricity use in Texas, advancing carbon-free electricity through renewable generation and other technologies. This investment is intentionally designed with flexibility in mind, allowing Microsoft to adjust capacity over time as demand evolves.

At launch, the datacenter campus will operate with a co-located natural gas power facility, an arrangement known as “behind the meter.” This serves the campus directly and independently of the public grid, so this demand does not take from the current grid. The plant’s design will integrate state-of-the-art air emissions controls, such as Selective Catalytic Reduction systems to lower nitrogen oxide emissions. Over time, we anticipate connecting the power facility and the datacenter to the broader grid and becoming part of the regional energy system, working in close coordination with utilities and local authorities. We will continue to drive additional improvements in environmental performance in line with our corporate commitments. This evolution reflects our long-term mindset in the region: as we grow, we intend to contribute to a more resilient and reliable grid that delivers value not only to our operations, but to the wider West Texas community.

Water stewardship
We plan to deploy closed loop cooling systems, which significantly reduce water requirements. This approach is expected to limit water usage by requiring only an initial charge of the cooling system at the start of operations, with no additional water consumption during steady-state operation. As a result, the total lifecycle water use of this datacenter is only a fraction of that consumed annually by a typical fast-food restaurant.

We are also designing our operations to minimize reliance on freshwater sources by utilizing nonpotable water where possible, helping to reduce pressure on shared community resources.

This builds on the way we approach water stewardship across Texas. Near San Antonio, Microsoft has helped fund the permanent protection of more than 1,500 acres in the Edwards Aquifer recharge zone — safeguarding a critical water source for over two million Texans — as part of our broader commitment to be water positive by 2030. In Pecos, we will continue to prioritize responsible water use, efficient design and close coordination with local authorities as our operations grow, and we will share our progress with the community over time.

The datacenter in Pecos represents an important step forward in how we build infrastructure for the AI era by combining capacity at scale, energy and a commitment to responsible growth.

But just as importantly, it reflects how we do this work: in partnership with communities, with an enduring mindset and with a focus on creating shared value.

As we move forward, we will continue to engage closely with the community in West Texas, provide updates on our progress and ensure that this investment delivers lasting benefits for both our customers and our neighbors. Community members can learn more at Open letter to Pecos and Reeves County – Microsoft Local.

We look forward to building that future together.

Noelle Walsh leads the organization that powers the global Microsoft Cloud. She oversees the company’s physical cloud infrastructure and operations, with a charter focused on safety, security, availability, sustainability and competitive infrastructure growth — bringing decades of global operational leadership.

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