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? [Read more]

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.

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[Bill James](https://www.linkedin.com/in/billljames?trk=public_post_comment_actor-name)2h

So, if consumers spend peanuts they’ll get AI monkeys.

Satya Nadella - One idea that stood out is that competitive advantage is shifting beyond the model itself. The organizations that win will be those that continuously improve the entire system, context, memory, workflows, evaluation and user feedback. AI is becoming less about choosing the “best model” and more about building the best learning ecosystem around it.

Satya Nadella AI is entering a phase where competitive advantage won't come from using the biggest model, but from using the right model for the right task at the right cost. Execution efficiency is becoming the new innovation.

Satya Nadella When you say models are trained against the actual product harness, what does that training loop look like? Is it reinforcement learning on real user interactions or something more supervised?

I think this is where the conversation is evolving. The model itself is becoming just one component of the system. In enterprise environments, the real differentiators are context, orchestration, governance, memory, proprietary data and how effectively AI integrates into existing workflows. The future belongs less to the "best model" and more to the best AI systems.

Excellent insights, Satya Nadella . As AI becomes increasingly model-independent, do you see an organization's greatest long-term competitive advantage shifting from the model itself to the quality of its proprietary data, context, workflows, and evaluation systems? A fascinating perspective on the future of enterprise AI. 👏🚀

Ankit -3h Frontier innovation scales only when cost-to-outcome is optimized across the entire agentic system, not just the model. By externalizing harness, memory, and evals, enterprises gain control and ensure hill-climbing continues independent of any single frontier. The real opportunity is to optimize across layers and route intelligently—turning frontier breakthroughs into ecosystem-wide value at lower cost. Satya Nadella

Satya Nadella Model independence combined with proprietary evals is how enterprise leaders protect their alpha. The core insight here is that the true competitive moat isn't the base model - it's the underlying data lineage, contextual memory, and feedback loops (RLEs) built inside the enterprise tenant boundary. Making these orchestration capabilities natively available in tools like Microsoft Foundry gives data and system architects the exact blueprint needed to build deterministic, cost-optimized agentic workflows.

Abi H.2h When software and AI models approach zero marginal cost, defensibility shifts entirely from code to execution and workflow integration. However, in the MEA region, the ultimate moat is not software scaffolding, it is physical infrastructure. You cannot deploy zero marginal cost software without guaranteed, high-density power and derisked capital structures. The real winners will not just integrate applications. They will structure the energy, sovereign compliance, and grid access needed to run them at scale.

Satish Saka2h "Evals should keep hill-climbing even when the model gets swapped out" is the principle that matters most here, and it generalizes beyond Microsoft's stack. Build your measurement layer independent of whichever model happens to be winning this quarter, or your whole system's progress is hostage to someone else's roadmap.

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