McKinsey's Cost of Compute AI Infrastructure Analysis

Capex will get more expensive with tariffs adding costs to datacenters and AI Infrastructure. Multiple bottlenecks exist for demands for compute not to be met at some point in the near future.

Good Morning,

Today’s article goes into our Datacenter section of the Newsletter.

I have been following BigTech Earnings closely for Cloud growth vs. AI capex to better understand the state of datacenters in 2025. Keep in mind while Microsoft and Amazon have pulled back from Datacenter plans, the cost of AI infrastructure is also going up with trade tariffs presenting a rather confusing puzzle:

Image by

Eric Flaningam

of Generative Value Newsletter.

Generative Value, Eric Flaningam

Meanwhile, McKinsey also put out a rather baffling report about the costs of AI Infrastructure by 2030:

Infrastructure Demands strain the World’s Capability

• Their research shows that by 2030, data centers are projected to require $6.7 trillion worldwide to keep pace with the demand for compute power. 🌏

• Data centers equipped to handle AI processing loads are projected to require $5.2 trillion in capital expenditures, while those powering traditional IT applications are projected to require $1.5 trillion in capital expenditure.

• US energy demand will surge over the next few years, with the sector facing six critical challenges. This could accompany surges in the cost of energy and inflation over global tariffs.

So the reason this is alarming is the uncertainty of it all - according to the Register: “A report from consultancy McKinsey & Company highlights the widespread unease over AI, pointing to the bewildering sums being invested into infrastructure to support it, while warning that forecasts of future demand are based on little more than guesswork.”

AI Capex is Accelerating Cloud Growth Modestly

Eric Flaningam’s analysis shows that combined, the three biggest cloud providers (AWS, Azure, GCP) are at a ~$247B run rate, growing 23.9% Y/Y. This is in part boosted by AI adoption and the Cloud growth has to be factored into the ROI from these massive datacenter investments. For the three main cloud leaders of Microsoft, Google and Amazon - this is massive growth, sped up by AI to a considerable degree.

However Amazon’s AWS growth is slowing down of the three the most: AWS revenue grew just 17% during the quarter. But it’s also the biggest.

So what conclusions can we draw from McKinsey’s analysis of the future of AI Infrastructure? It admits these projections are highly speculative.

Sam Altman’s 7 Trillion Quote finally Makes Sense

In February 2024 Sam Altman made some claims that the media took a bit out of proportion about OpenAI’s Stargate ambitions, now it finally makes sense. However it’s still such an enormous sum the energy bottleneck and who can follow are major problems.

McKinsey: “Overall, that’s nearly $7 trillion in capital outlays needed by 2030—a staggering number by any measure.”

Is BigTech going to subsidize access to AI of the future? And who will be able to follow?

• To meet this demand, companies across the compute power value chain run the risk of over-extending themselves.

McKinsey

• If you are an investor in BigTech or the “Magnificent seven”, you might want to read this.