$100bn (cloud) baby

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AWS is now near a $100bn run rate. It is also highly profitable, contributing 76% of all Amazon’s operating profits over the past decade. Azure became Microsoft’s largest product line in terms of revenue in 2019, and that business still grew 30% year-on-year. Google, the smallest of the big cloud providers, clocked up a 25% annualised growth in the fourth quarter. Overall these three hyperscalers added $15bn in new annual recurring revenue in the fourth quarter of last year.

Every time a company announces its intention to invest in AI (whether generative or some other flavour), these workloads must run somewhere. For mainstream businesses, that somewhere will often be the cloud; despite rising costs, only the bravest or most capable can take their infrastructure back in-house.

There is a long-term secular trend for companies to do more in silico. Today, we might think of AI workloads as being about cost optimisations and efficiencies. But increasingly, the real value-added work of a company (whether it is route planning, pricing analytics, research and development) will run on demanding ML models. The clouds will continue to rise.

This is an excerpt from my Sunday newsletter. Continue reading here.