Microsoft ramp up AI spending as it smashes Q3 earnings expectations

Microsoft’s decision to invest heavily in artificial intelligence (AI) appears to have paid off after the company revealed significant gains in its third-quarter earnings call on Thursday (25 Apr).

The world’s largest public company outperformed Wall Street expectations, delivering $61.86 billion in revenue after billions of dollars were invested in AI but the spending is set to continue.

Demand for AI is soaring and Microsoft has vowed to boost capital expenditure to scale up its cloud platform, Azure. It has benefited from increased spending from the client base, with $26.7 billion in revenue recorded on cloud products, overall.

The upturn meant the group’s share stock spiked 4% in after-hours trading, with Microsoft CEO Satya Nadella stating Microsoft’s AI tools “are orchestrating a new era of transformation, driving better business outcomes across every role and industry.”

Microsoft well placed as major companies compete in AI development

With its stock market value of almost $3 trillion, Microsoft looks to be the frontrunner in the AI sector, boosted by an increase of 35% in its shares over the last 12 months. However, this significant gain has been beaten by Amazon and Google, with their stocks growing by more than 65% and 51%, respectively in one year.

Meta is also striving to keep pace in the rapidly evolving AI race, as its CEO Mark Zuckerberg sounded a warning to investors that it will take considerable time before any significant returns are made on generative AI after he committed the company to further spending, ahead of a period of lower profit forecasts.

AI is a catalyst and key driver for much of the growth at present, as witnessed by Microsoft’s multi-billion dollar investment in OpenAI, the maker of ChatGPT.

These ambitions require great resources and a vast amount of power. Microsoft aims to acquire 1.8 billion semiconductor chips by the end of this year as well as to turbocharge its data center capability. Microsoft is also creating its own chips to meet the excessive requirements as AI models need to be trained on huge amounts of data.

Image credit: Ideogram