Microsoft (MSFT) has been focusing on monetizing AI across its product portfolio, leading to a new record high stock price of $433.60. With shares up 10.4% year-to-date, the company is seeing increased success in the AI space. By offering platforms and tools for customers to build their own AI solutions, Azure, Microsoft’s cloud service, is expanding its market share. In fiscal Q3, Microsoft saw a significant increase in large Azure deals, with the number of $100 million+ deals rising more than 80% year over year.
The growth in Azure revenue contributed to Microsoft Cloud revenue increasing by 23% to $35.1 billion in FQ3. Azure revenue growth of 31% surpassed expectations and the consensus estimate. The company is expected to see continued growth in Azure revenue in FQ4, with estimates pointing to a 30% to 31% growth rate. The strong performance in Azure led JP Morgan to raise its price target for Microsoft to $470, citing the potential for further growth in the next 12 months.
Microsoft’s total commercial bookings grew by 31% in constant currency in FQ3, a significant acceleration from the previous quarter. Commercial revenue performance obligations (RPO) also saw a sharp acceleration in growth, reaching $235 billion. Overall, Microsoft’s total revenue increased by 17% to $61.9 billion, surpassing analyst expectations. Morgan Stanley and Goldman Sachs have both raised their price targets for Microsoft, highlighting the company’s growth potential in the AI space.
On the FQ3 earnings call, Microsoft CEO Satya Nadella emphasized the importance of AI in driving growth for Azure. He mentioned that Azure has become a top choice for AI projects, attracting new customers and expanding its existing base. Nadella also highlighted the interconnected nature of AI projects, which often require additional services like developer tools and Azure Search. He emphasized the role of AI in driving innovation and growth for Microsoft.
Microsoft has introduced a new class of small language models (SLMs) that offer similar capabilities to large language models (LLMs) but require fewer computing resources to operate. These SLMs, such as the Phi-3 family, outperform models of similar and larger sizes across various benchmarks in language, coding, and math capabilities. SLMs are designed for organizations with limited resources and simpler tasks, offering a more accessible option for AI applications.
There is a long-term opportunity for more capable SLMs to be deployed on devices at the edge, such as smartphones and other IoT devices. This would enable AI-powered applications in areas like automotive computers, traffic systems, factory sensors, remote cameras, and environmental monitoring devices. By keeping data on the device, latency can be minimized, and privacy can be maximized. Microsoft’s focus on advancing AI technology through SLMs is positioning the company as a leading innovator in the AI space.
