Amazon and Microsoft cloud growth helps ease worries about AI profit paths, investor says

Market participants have increasingly focused on a basic question for the artificial intelligence era: if building ever-larger machine-learning models becomes harder, slower, or less certain, can the biggest cloud providers still profit from AI demand? In commentary shared with investors on July 31, Amati Global Investors’ Mikhail Zverev pointed to Amazon and Microsoft’s fast-expanding data-center businesses as evidence that hyperscalers can still convert AI interest into financial results.

Zverev’s view, reported by Yahoo Finance, is framed around how companies that sell cloud computing services translate AI workloads into revenue. Even if the industry’s attention remains on model development, he suggested that the underlying cloud infrastructure buildout is where much of the near-term monetization is taking place, as enterprises and developers need computing capacity to train and run AI systems.

The argument centers on the companies’ “booming” cloud operations and their data-center expansion, which Zverev described as directly answering the market’s concern that AI profitability might depend too heavily on constant progress in new model releases. The implication is that a growing installed base of capacity can support demand for AI services, regardless of whether the next wave of frontier models arrives quickly.

For Microsoft, the same theme plays out through its Azure cloud platform and its broader push to bring AI tooling to enterprise customers. For Amazon, the parallel is its AWS cloud business, which offers managed services that customers use to deploy and operate AI applications. In both cases, Zverev’s point is less about whether a specific new model is announced and more about whether customers are buying compute, storage, and related services tied to AI workloads.

The reported commentary does not include specific financial figures, margin impacts, or growth rates. It also does not provide a timeline for when data-center investments translate into higher earnings, beyond the general characterization that the companies’ cloud businesses are expanding rapidly.

Still, Zverev’s framing fits a broader market debate that has emerged as AI adoption becomes more operational for businesses. The early period of AI spending was heavily associated with model creation. The next phase increasingly involves running production workloads, optimizing costs, and scaling infrastructure so companies can serve AI features reliably.

What remains unclear from the reported post is how durable the profit announcement is, particularly if AI demand cycles, hardware pricing changes, or competition intensifies. The commentary also does not specify how much of the companies’ growth is attributable to AI versus other enterprise and consumer cloud applications, nor does it break out the contribution from different AI services.

Investors are likely to look for follow-through in future updates on cloud capacity utilization, data-center spend, and the extent to which AI workloads are supporting sustained revenue growth. For Microsoft and Amazon, the next measurable indicators to watch are cloud growth trends, commentary on data-center investment returns, and evidence that enterprise AI deployments continue to translate into incremental demand for cloud services.