TL;DR ELI5 of @satyanadella new post: the best AI product strategy is no longer renting the biggest model. It's training small in-house models inside the product until they match frontier quality on everyday tasks. 🧠 Frontier models are amazing but expensive. Most everyday tasks don't need them 🏋️ Microsoft trained small MAI models inside the actual products (GitHub Copilot, Excel) using RL environments that reward completing real customer tasks, not benchmarks 📊 The MAI model in Excel matches GPT-5.6 quality on the most common tasks at a fraction of the cost, and runs on older H100/A100 GPUs instead of the latest chips 🔀 They now route traffic to MAI wherever it matches frontier quality, and only call OpenAI/Anthropic models for true frontier needs 🧩 The trick is the system, not the model: harness, memory, context, tools, and evals all live outside the model, so any model can be swapped in or out and the product keeps improving 📈 Code model became the Excel model: MAI-Code-1-Flash was the starting checkpoint for the Excel climb, going from ~72% to 86% on their evals 🏢 Same playbook every enterprise can run with their own evals, RL environments, and workflows Small models trained in the product, frontier models on standby. That's the whole strategy.