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Performance now depends on the type of output rather than which model is currently on top. That's Andreou's case for a multi-model approach that routes each task to the model that fits best, while still letting people choose a specific model if they want.
Microsoft doesn't make either model Andreou names, so this is also a description of its own position. Its vision is that the product around a model will matter more than the model, and that the app you work in will shape your results more than the lab behind it.
Microsoft doesn't make either model Andreou names, so this is also a description of its own position. Its vision is that the product around a model will matter more than the model, and that the app you work in will shape your results more than the lab behind it.
Most talk about agent risk centers on what an agent can access. Andreou's home-network story is about what an agent does when its first plan fails. If you're testing agents at work, list the actions that can't be undone before you start, and ask how the tool behaves when it gets stuck.
Being AI-native still comes down to human judgment and decision-making. The research leader at Microsoft didn't need to know how to code, but did need the expertise to decide what was worth building.