Microsoft wants to build its own advanced AI models by 2027 at the level of OpenAI and Anthropic

Microsoft wants to develop its own large frontier-level AI models as early as 2027. In essence, the company is trying to build an in-house alternative to the most powerful systems from OpenAI and Anthropic — not as a supporting tool, but as a full-fledged technological foundation for the next stage of the AI race. This is an important signal for the entire market.

Until now, Microsoft has been seen primarily as one of the main infrastructure and product players in AI: the company is actively integrating AI into its services and building an ecosystem around it. Now the focus is shifting toward creating its own foundational models capable of competing at the top end. This is not about small applied solutions, but about large models that set the bar for quality, breadth of tasks, and level of autonomy.

The report suggests that Microsoft's goal is to have an internal alternative to the strongest models from OpenAI and Anthropic as early as next year, that is, by 2027. For the company, this is not just a matter of prestige. A proprietary model of this class gives it control over a key layer of the AI stack: architecture, development priorities, update speed, and the way it is integrated into its own products.

The less dependence there is on an external supplier of the base model, the more flexibly strategy can be built and the faster decisions can be made. Another important point is the competition for control over the economics of AI. Large models require enormous compute resources, expensive infrastructure, and long research cycles.

If Microsoft can bring its developments to the frontier level, it will be able not only to use them inside its own services, but also to offer customers a more tightly integrated bundle of cloud, development tools, and the model itself. This is especially important for the enterprise market: businesses need not only strong AI, but also a clear vendor that is responsible for several layers of the technology platform at once. The timeline is also telling.

When a company sets 2027 as its target, it implies a fairly intense pace of work: at this level, it is not enough simply to assemble a team and release a demo. What is needed are mature research approaches, stable access to compute, a well-tuned training process, and the ability to improve the model quickly after the first launches. In other words, Microsoft wants not just to catch up with the trend, but to join the group of companies that determine what the next wave of generative AI will look like.

It is also important that an internal alternative does not necessarily mean an immediate end to cooperation with external labs. Rather, it is about a redistribution of the balance of power. When a company has its own strong model, it gains more freedom in negotiations, more room for experimentation, and fewer strategic risks.

This is especially valuable at a time when the market is changing very quickly, and access to the best models is becoming critically important for both consumer services and enterprise software. Against the backdrop of rapid growth in generative AI, such a move looks logical. OpenAI and Anthropic are currently seen as the benchmark in the segment of the most powerful models, and Microsoft's attempt to build a comparable system in-house shows that partnerships and integrations are no longer enough.

Market leaders want to own not only the distribution channel, interface, or cloud, but also the core of the technology itself. This increases business resilience: if external partners change priorities, prices, or access terms, the company retains its own strategic resource. For users and companies building products on top of AI, this could mean more choice and tougher competition among base-model providers.

And for Microsoft itself, the success of such a project by 2027 will signal that the era of dependence on someone else's frontier models is coming to an end: the largest technology platforms are striving to create their own intelligence in-house.