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Microsoft’s AI masterplan: Let OpenAI burn cash, then build on their successes

Apr 07, 2025 - theregister.com
Microsoft AI CEO Mustafa Suleyman has articulated a strategy of playing a close second in the generative-AI race, emphasizing cost-effectiveness and optimization for specific customer use-cases. Instead of directly competing with frontier model builders like OpenAI, Microsoft leverages its partnership with OpenAI, providing Azure cloud compute in exchange for using OpenAI's GPT models in its Copilot AI services. This approach allows Microsoft to focus on building applications and systems around language models rather than developing new models from scratch. Suleyman acknowledges the importance of eventually achieving AI self-sufficiency at Microsoft, but the partnership with OpenAI is expected to continue until at least 2030.

In addition to its collaboration with OpenAI, Microsoft is developing its own line of smaller, permissively licensed language models under the Phi codename, which are suitable for edge devices. These models, while not as feature-rich as OpenAI's offerings, are cost-effective and efficient. Microsoft is also investing in research and frameworks to integrate AI models into enterprise systems, such as Autogen for orchestrating AI agents and KBLaM for extending a model's knowledge with structured data. This systems approach allows Microsoft to enhance the utility of AI in practical applications.

Key takeaways:

  • Microsoft's AI strategy, led by CEO Mustafa Suleyman, focuses on trailing frontier model builders like OpenAI by three to six months to optimize cost and customer use-cases.
  • Microsoft's partnership with OpenAI allows it to leverage the GPT family of models for its Copilot-branded AI services, while also developing its own smaller Phi models for edge devices.
  • Suleyman emphasizes the importance of Microsoft eventually achieving AI self-sufficiency, although its partnership with OpenAI is expected to continue until at least 2030.
  • Microsoft is investing in systems and frameworks, such as Autogen and KBLaM, to integrate AI models into enterprise systems more efficiently, alongside developing open-source tools like VidTok.
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