Building a hill-climbing machine: Launching seven new MAI models | Microsoft AI

# Building a hill-climbing machine: Launching seven new MAI models | Microsoft AI Source: [https://microsoft.ai/news/building-a-hillclimbing-machine-launching-seven-new-mai-models/](https://microsoft.ai/news/building-a-hillclimbing-machine-launching-seven-new-mai-models/) Alongside distribution on Foundry and optimization for our 1P products, our models are also going to be widely available for developers on Open Router, as well as[Fireworks](https://openrouter.ai/microsoft/mai-image-2.5)and[Baseten](https://www.baseten.co/blog/mai-thinking-1/)\. For the first time developers will be able to tune the weights of the model themselves\. All these models are built on a shared foundation, hill\-climbing from the bottom with zero distillation\. They share the same data discipline, the same infrastructure and the same evaluation framework\. They are designed to work together, and to integrate directly into the products people use every day\. But the models themselves are only part of the story\. The most important shift lies in what you can do with them\. ## Adapted for you AI is moving into a new phase\. With reinforcement learning in real\-world environments, AI can fully adapt to the specifics of a given workflow for the first time\. We call this Microsoft[Frontier Tuning](https://learn.microsoft.com/en-us/microsoft-365/copilot/copilot-tuning-overview)\. We think it’s the future for how AI shows up\. In this set\-up the most valuable data is yours: the trace of real work an agent completes, the sequence of steps, the decisions, the actions taken that define how tasks actually get done inside an organization\. Our reinforcement learning environments \(RLEs\) allow your MAI models to learn directly from your workflows\. Think of them as training gyms for AI, accessible only to you\. With Frontier Tuning, you’re building your own model, trained on your data, within your environment, controlled by you\. Your institutional knowledge becomes part of the model, and it stays yours\. What’s even better is that this adaptation then drives efficiency and performance\. Across Microsoft and with customers, Frontier Tuning is showing that custom models are both better and more efficient: our MAI tuned model for Excel matches GPT 5\.4 while being up to 10× more efficient\. Early adopters are seeing similar gains at the frontier\. When tuned for McKinsey’s exacting enterprise standards, MAI achieved the highest win rate of any model tested at roughly 10× lower cost\. Developers and businesses have been crying out for AI that delivers on their terms and under their say\. We see this as a major step towards delivering that\. ## Frontier health intelligence with the Mayo Clinic There are a number of high importance, high sensitivity domains – like health – that require even more intense collaboration\. That’s why today we’re also announcing that Microsoft and Mayo Clinic are collaborating to co\-create a frontier AI model for healthcare that brings together Mayo Clinic’s world\-leading clinical expertise, de\-identified clinical data and longitudinal insights with Microsoft’s foundational AI capabilities\. This model will be designed to excel at the broadest scope of clinical reasoning and healthcare use cases, reaching a level that today’s general\-purpose systems simply cannot match\. The model will first be deployed within Mayo Clinic’s own environment, the world’s top hospital system, where we expect it to enable a broad range of capabilities, including earlier and more accurate diagnoses and treatment planning\. Once validated, the model will be made available to other organizations via Azure Foundry, making Mayo Clinic’s expertise accessible to many more who need it\. The frontier AI model will be owned by Mayo Clinic, reinforcing our mutual longstanding commitment to patient trust, clinical rigor, safety and responsible stewardship of clinical health data and AI\. ## Our Lab At Microsoft AI, we recognize that there are no shortcuts to the frontier\. We train from scratch\. We don’t distill from other labs and we don’t rely on unlicensed or opaque data\. Our datasets are clean and appropriately licensed\. Every component of the system, from architecture to training pipeline to post\-training, we built ourselves\. We co\-design with our own Maia 200 silicon, and are already seeing a 1\.4x efficiency boost from these efforts\. This is all about long term self\-sufficiency for Microsoft and our partners\. It’s about models you can trust\. The goal here is to build what we think of as a hill\-climbing machine: an organization that can continuously improve, cycle after cycle, as we apply more compute, better data, and sharper evaluation\. We think scientific rigor is critical to this\. That’s why for everything we do, we ablate, we measure, we document\. We invest heavily in data pipelines\. We work in small teams with falsifiable goals over short time periods, matching velocity with quality, focus with ambition\. And we are committed to transparency\. We want to bring you with us on this journey\. That’s why today we are publishing in depth safety and technical reports\. ## *Humanist Superintelligence* This is what we are building in MAI: a family of models, with new versions available now\. A lab built on first principles, focused on long\-term capability\. And a new approach to tuning and ownership that we believe will define the next phase of AI\. Our ultimate goal is what we call Humanist Superintelligence\. That means advanced AI systems designed to serve people and organizations, not replace them\. These systems must remain tools, shaped by human intent, accountable to human oversight, and ultimately subordinate to human goals\. People – you – must always remain in control\. Over the next year, stand by for a rapid scale\-up in our compute and capabilities as we push to make this ambition a reality\. It’s a new phase for AI, and for us\. – The MAI Team