According to Woofun AI, Microsoft (MSFT.US) has officially established the Maia accelerator family as its core inference infrastructure. JPMorgan (JPM.US) subsequently released a research report maintaining an Overweight rating on Microsoft and setting a price target of $625. The core rationale behind this investment decision is that Microsoft is fundamentally reshaping the cost-efficiency model of its AI services through large-scale deployment of its in-house chips, rather than relying solely on external compute procurement. Market focus has shifted from mere revenue growth to structural improvements in capital expenditure efficiency and cloud business gross margins.
A substantial breakthrough in technological iteration occurred on October 12, when Microsoft announced that the Maia accelerator family would scale to multi-gigawatt deployments starting in 2027, officially becoming its primary inference system for products and services. The third-generation product, Maia 300, is scheduled for production in 2027 and delivers 40% more tokens per megawatt than leading GPU systems, achieving seven times the token throughput of the previous-generation Maia 200 in a fully configured rack with 96 accelerators.
Notably, the Maia 200 delivers 1.7 times more tokens per watt in its built-in AI capabilities compared to traditional GPU infrastructure. This performance leap is not accidental but results from deep innovations in software-defined data flows and computing architecture. At the hardware level, the chiplet architecture significantly enhances high-bandwidth memory (HBM) bandwidth per watt, combined with a data center-scale vertical scaling network to achieve exponential growth in compute density.
Currently, Maia 200 fully supports production workloads for Copilot, with GitHub Copilot and Microsoft Foundry expected to follow by the end of 2026. Microsoft has accumulated extensive experience supporting OpenAI models, MAI models, and leading open-source model families. J.P. Morgan believes that Maia’s transition from an auxiliary role to a primary inference system marks a pivotal turning point in Microsoft’s AI infrastructure strategy.
Woofun AI's aggregated data shows that this architectural innovation directly reduced per-inference costs, laying the technical foundation for large-scale deployment.
Economies of scale are another core driver of cost optimization. According to Reuters, Microsoft plans to deploy over one million Maia 300 chips by 2027 to support its internal AI services. Meanwhile, Microsoft’s infrastructure expansion plans are equally aggressive, with expectations to more than triple global data center capacity by 2032; J.P. Morgan forecasts that capacity will exceed 30 gigawatts by 2030.
This massive deployment at the million-chip scale, synchronized with data center capacity expansion, aims to optimize the overall cost structure through economies of scale. Financially, JPMorgan expects Microsoft’s capital expenditures to be approximately $210 billion for fiscal year 2027 and around $175 billion for the calendar year 2026.
Although the absolute amount of capital expenditure is substantial, Maia’s deployment primarily focuses on internal use cases and does not aim to generate external computing revenue, meaning investors should closely monitor changes in the intensity of capital expenditure and the marginal cost of cloud software sales. The deployment of millions of chips corresponds to multi-gigawatt capacity; a 40% increase in tokens per megawatt for Maia 300 means significantly higher inference throughput under the same power constraints, thereby reducing unit computing costs at a macro level.
From a long-term financial perspective, the full-scale deployment of Maia as the primary inference system will directly improve the profitability quality of Microsoft Cloud. Currently, Microsoft Cloud’s gross margin sits in the mid-to-high range of 60% to 70%; J.P. Morgan believes this metric has further upside potential as Maia deployment deepens. Since the focus of deployment is on internal use cases rather than external compute revenue, the return on capital expenditures will be reflected primarily in lower software costs for cloud services and improved operational efficiency. The reduction in inference costs and the improvement in cloud gross margin together form the dual drivers of Microsoft’s long-term competitive advantage in AI infrastructure. J.P. Morgan maintains an Overweight rating based on its confidence in this cost-optimization trajectory. In the future, with the full-scale production of Maia 300 in 2027 and continued expansion of data center capacity, Microsoft’s cost advantages in the AI era will become even more pronounced—not only a result of technological superiority, but also a reflection of strategic discipline and execution capability.