Microsoft

Just wrapped our earnings call. It was a very strong close to what was a record fiscal year for Microsoft. · Annual revenue: $331B, +18% · MS Cloud: $214B, + 27% · And Azure: $100B+, +41% And even bigger opportunity ahead! I wanted to share some more perspective on two areas of focus for us: First, we are building a new model system, where the harness, context, memory, and action space are separate from any one model family, thereby moving the frontier on the cost -to-outcome curve. And it’s not just about cost. It also has the added benefit of business continuity and resilience because every model is substitutable. This is the system we are using in our products, with great results. And we are making it available for our customers via Foundry too. You can read more about this from Mustafa Suleyman here: https://lnkd.in/gQwfAbuc Second, when it comes to Copilot, we are innovating rapidly, from chat to Cowork to Autopilots. We have also been super focused on improving the quality and performance of Copilot. Over the last three quarters, user satisfaction scores have doubled. And this quarter alone, we cut latency by 25 percent. Copilot usage intensity speaks for itself: · Number of conversations per user nearly doubled year over year · Average weekly engagement on par with Outlook and Teams · Number of customers with over 50K seats up 7X Y/Y · Customers deploying Copilot to majority of information workers, up nearly 75% QoQ And this quarter we will bring all our Copilot experiences together in one “super app” spanning both our consumer and commercial experiences. I’m greatly looking forward to this! A big Thank you to our employees and to our customers for continuing to put their trust in us. Read more here: https://lnkd.in/gR3xdrUb

Doctors are busy, visits can seem rushed. Many people – especially women – can feel like their doctor hasn’t really heard them.   In July's edition of The Monthly Tech-In, we explore how AI is helping clinicians spend more time listening to their patients and less time on administrative tasks. Beyond healthcare, we share stories about how farmers, researchers and executives are using AI to meet real-world challenges.    Learn more about the people putting AI into practice.

Today, we are announcing a series of updates that give customers frontier-grade security at half the cost. MAI-Cyber-1-Flash is our first cybersecurity model, built ground up to find the most challenging vulnerabilities in complex code bases. When combined with MDASH, it delivers world-class performance at 50 percent of the cost of leading models. We are bringing this capability to market through Project Perception, a complete agentic security offering grounded in real-world signals and security workflows. Teams of specialized agents work together to simulate attacks, detect and triage/investigate, and fix and remediate. This is the benefit of building the harness, context/signals, and action space separate from one model family. By combining specialized models and data with the right agents, tools, security context, and harness, we can advance the frontier of cost to outcome. Learn more: https://lnkd.in/gZHUCuQp

The measure of AI isn't what it finds. It's what it helps customers prevent. Using Project Perception, Nationwide Building Society compressed what was once four weeks of threat intelligence analysis into four hours, and they are using these insights to take action. I'm grateful to our partners at Nationwide for helping us shape Perception’s development through candid feedback and real-world testing.    https://lnkd.in/gd-_JFut

AI is changing the speed, scale, and economics of attacks. What worked for security in a world of human actors struggles in a world of AI, agents, autonomy, and continuous adaptation. We believe security needs a new Cyber Stack. One that will empower defenders with real-time protections, defending against AI with AI. That conviction led us to build Project Perception, a new agentic security system designed for the realities of AI, in public preview August 3. What sets Perception apart is its combination of Security Context, a Multi-Model Harness, and Specialized Security Agents. Earlier this year, we introduced MDASH, our software vulnerability multi-modal team of agents. MDASH launched beating Mythos, GPT and Gemini on CyberGym, the gold standard benchmark for evaluating how systems reason over large codebases to find real vulnerabilities. MDASH reinforces our belief in a multi-modal approach and that you deliver the best results by applying the right model, to the right task, at the right time. And this is why we are partnering with Mustafa Suleyman and the MAI team to continue to push the limits with MAI Cyber-1-Flash inside MDASH. Together, MDASH with MAI-Cyber-1-Flash deliver 96% on CyberGym (+12 pt above Mythos) at 50% of the cost when compared to our existing MDASH offering (MDASH + GPT 5.4 + 5.4 mini + 5.3 Codex). There should not be a choice between innovation and safety. Security should be an accelerator, not a blocker. Welcome to Perception. https://lnkd.in/gX7XN4xZ

As the new The Premier League season approaches, I'm delighted to share that Fantasy Premier League (FPL) managers are getting a new team-mate this season. The Fantasy Premier League Companion, powered by Microsoft Copilot, is designed to help managers make more informed decisions every week, bringing together official Premier League data, personalised team context, and conversational #AI to make the game easier to understand and more engaging to play. Whether you’re new to FPL and need help understanding the rules of the game, formations or scoring, or you’re an experienced manager looking to compare strikers by shots in the box, fixtures or form, the Companion is built to provide useful insights quickly and clearly. Importantly, it’s not there to run your team for you. As Andy Selby, senior gaming manager at the Premier League, explains, the goal is to give managers better information, options and context - while keeping the final call firmly in their hands. It’s a brilliant example of how AI can turn complex, fast-moving data into practical, personalised insight at scale, and deepen the connection fans have with the players, clubs and stories they follow across the season. Read the full interview with Andy Selby here: https://lnkd.in/e5ejatBk #PremierLeague #FantasyPremierLeague #MicrosoftAI #Copilot #AI #Football Will Brass | Richard Masters

In a world where software has real marginal cost for the first time, how do we ensure frontier benefits are diffused across the entire ecosystem? The key is to optimize the cost-to-outcome frontier in real world context. In practical terms, that means using the right model for each task, and optimizing the context, skills, tools, and agent harness around it. This is the motivation behind our MAI model family. These models have been built ground up with clean data lineage and optimized for learning transfer from generalist to specialized skills in enterprise RLEs. We continue to make rapid progress in this pursuit. We can now take saturated frontier capabilities and deliver them at scale and at lower cost through models optimized for high-usage products, while continuing to use frontier models for frontier needs. We are proving this out across our first party products, and thereby creating a template for every other AI native, SaaS, or Enterprise company out there. In our products, frontier models from OpenAI and Anthropic are part of the orchestration system alongside MAI. But the model is only one part of the hill-climbing system. Harness, memory, context, tools, skills, user interactions, etc. all shape the evals and performance of these agentic systems. The other key criteria to ensure that you are in control, is your evals should continue to hill climb even when any given model has been removed. Therefore we build RLEs where models learn inside the product system and are rewarded for completing the tasks customers actually care about. We train models against the actual product harness, interactions, and outcomes they will encounter. And strategically ensure that the harness, memory, context, skills are externalized outside of the model. Product-specific evals and model independence give us the control and a direct hill to climb, and to keep refining until we reach the right quality-cost target. We are now seeing MAI models outperform general-purpose frontier models in many use cases while using a fraction of the tokens. We believe the biggest opportunity is to optimize all of these layers together in the products where the world works every day. And we are beginning to route traffic across our first-party surfaces to MAI whenever our models match or outperform frontier alternatives. We are seeing promising early results across GitHub Copilot, Excel, and Outlook and are beginning to take the same approach across Copilot Chat, PowerPoint, and more. And all these results will only get better as the entire system keeps hill-climbing! What we are doing across our first party products is also what every enterprise customer can be doing in their real world agentic systems with their proprietary evals, their proprietary RLEs, workflows, and context. We are making all this available as part of Foundry and our toolchain. Read more here: https://lnkd.in/gR7UgHkp