Microsoft's AI Strategy -- Copilot, AI Infrastructure, and the Fast-Follower Challenge | Part 1

In late February 2025, Microsoft President Brad Smith discussed the company's AI strategy with former Australian Prime Minister Kevin Rudd at the American Australian Association in New York. This is Part 1 of a four-part series on what that conversation might mean for business and sport.

Training versus deployment

Microsoft's AI strategy rests on two areas: the resource-intensive work of training AI models, and inferencing — putting trained models to work in real applications. Smith noted Microsoft is providing infrastructure for AI leaders like OpenAI (including its ambitious, roughly $500 billion Stargate project) while also pushing its own initiatives, even while questioning whether spending at that scale is sustainable.

Copilot and the business model question

Microsoft has made Copilot its flagship AI product, integrated across Microsoft 365, Azure and enterprise applications. Smith sees the applications layer generating clear economic returns, but the picture is murkier for foundational models — especially since Meta's decision to open-source Llama has made it harder for players like OpenAI and Anthropic to justify premium pricing for proprietary models.

Open versus closed source

Smith outlined the divide between closed-source models such as GPT-4, Gemini and Claude, and open-source alternatives like Llama and DeepSeek, which deliver strong capability at a fraction of the cost. DeepSeek in particular built a competitive model with a lean team of relatively junior engineers, proving that agility and fresh thinking can matter as much as budget.

What's next

Smith expects the open-versus-closed debate to shape the next decade, with Microsoft positioning itself as a hybrid player investing in its own models while supporting both approaches.

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