Data Center Capacity Demand to Grow 3.5X Between 2025 and 2030, McKinsey Estimates

Momentary concerns about enterprise or hyperscale data center demand aside, virtually all observers might agree that demand for data center capability is going to grow substantially, to support artificial intelligence and computing workloads  in general. 

Consultants at McKinsey and Co., for example, estimate that between 2025 and 2030 data center capacity demand is going to grow by 3.5 times. 

source: McKinsey and Co. 

That, in turn, will drive an estimated $3 trillion to $8 trillion in additional data center capacity by 2030.

source: McKinsey and Co.

Are Government Home Broadband Networks Facing Worse Business Cases?

Dr. George Ford, Phoenix Center for for Advanced Legal & Economic Public Policy Studies chief economist, notes in a recent study that three sales of municipal home broadband networks illustrates the financial issues such networks face. 

The Bardstown, Ky. network, for example, privatized in 2024, illustrates the revenue side of the problem. 

source: Phoenix Center 

Another study looked at the financial performance of every municipal fiber project (with published financial data) in the U.S. operating in 2010 through 2019. None of the 15 projects generated sufficient nominal cash flow in the short run to maintain solvency without infusions of additional cash from outside sources or debt relief. 

To be sure, 68 operating networks provide no public financial information, some observers note. 

Similarly, 87 percent have not actually generated sufficient nominal cash flow to put them on track to achieve long-run solvency. 

Some 73 percent generated negative nominal cash flow over the past three fiscal years, leaving them poorly positioned to make up their deficits and causing them to fall farther into debt, the authors note. 

Fully 53 percent of projects would not be on track to reach breakeven even assuming the theoretical best-case performance in terms of capital expenditures and debt service.

Business Model Issue	Impact	Sources
Short-Term Revenue Shortfalls	Most projects fail to cover operating costs with subscription fees, requiring taxpayer subsidies.	2,4,10
Long-Term Viability Concerns	Only 2/15 projects studied showed potential for self-sustaining cash flow over 20-25 years.	2,6,10
Network Upgrade Costs	Frequent tech advancements require reinvestment, straining budgets not designed for dynamic needs.	137
Cross-Subsidization Risks	Many rely on municipal utility funds or bonds, distorting competition and transparency.	7,10,11
Crowding Out Private Investment	Municipal entry reduces private sector incentives to build/upgrade networks in the same areas.	3,6,11
Project Management Complexity	Lack of expertise in broadband operations leads to cost overruns and service quality issues.	3,5,9
Political vs. Market Incentives	Prioritizing coverage over profitability results in unsustainable pricing and service models.	3,10,11
Financing Challenges	Securing loans/investment is harder due to incumbent opposition and uncertain ROI.	5,9,11

AI Stack Will be Based on Layers, Just Like All Other App Ecosystems

“Digital infrastructure” tends to refer to physical assets like data centers, fiber optic networks, cell tower networks and cloud computing “as a service” providers, at least as seen by investors in and operators of such assets. 

And even the cloud computing providers (AWS, Zaure, Google Cloud and so forth) most often viewed as the key customers for digital infra providers, rather than core parts of the digital infrastructure business itself. 

However, as AI capabilities become more integral to business operations, there is an argument that digital infrastructure should also encompass AI as a service (AIaaS) capabilities which extend beyond hardware to include software models, language models, and AI platforms. 

Right now that is perhaps not the case, as investors and operators of language models tend to be distinct from traditional “digital infra” providers and investors. 

The “AI ecosystem” is different from the “digital infra” ecosystem, as the former necessarily includes chips, servers, language models, other AI systems and apps. 

As a practical matter, the expanded definition probably will not happen, for several reasons. For starters, financial analysts and operators of “computing” businesses tend to be different from “digital infra” analysts and operators. The former tends to be anchored by “software and hardware” interests, while the latter tends to be dominated by “real estate” interests. 

Foundation models and language models such as  GPT-4, Gemini, and Claude will tend to be the province of “software” industry analysts and practitioners; “hardware” analysts and practitioners such as Nvidia as well as the traditional “computing” ecosystem participants and analysts. 

In other words, the analysts and businesses that are in “middleware” and software stacks and tools, plus applications, are distinct from the analysts and businesses that supply “real estate” functions such as data transmission and data center operations. 

“Cloud computing” might be the function of many data centers, both including AI operators and all other software hosting, but there still are key differences between the businesses of computing and connectivity real estate and the actual software applications that use those real estate assets and platforms. 

So “infrastructure” and “applications” (including language models and AI as a service) will likely continue to remain separate areas of interest. 

How Much Will AI Compute Grow to 2030, Compared to Electricity Consumption?

A report by the International Energy Administration estimates data centers accounted for around 1.5 percent of the world’s electricity consumption in 2024, and might “more than double” to three percent of electricity consumption by 2030. 

What that means partly depends upon one’s perspective. Consider that estimates of AI-related compute cycles between 2025 and 2030 can range from growth of several hundred percent to 1000 percent (five times  to 10 times).

In that sense, an increase in use of electricity to power the servers handling those operations is relatively low, in relation to the increase in compute volume. 

Of course, the report also emphasizes that the outlook is “highly uncertain.” As always, much hinges on the assumptions about consumption growth and efficiency gains. 

source: IEA 

Ther study pegs data center electricity demand growth at more than space and water heating but less than space cooling or appliance use. 

source: Nature, IEA

By End of Year, Early 2025 Data Center and AI Capex Expectations by Hyperscalers Will Still Prove Correct

Much has been made in some quarters of reported data center and AI decisions by Microsoft  The scaling back of investment in such facilities has  raised concerns Microsoft is cutting back on capacity expectations for its global data centers. 

And that has raised questions about capacity oversupply in the AI data center area more broadly. That may not be the case. 

Analysts still broadly agree that Microsoft is committed to enormous capital expenditures on data centers and AI infrastructure. But reported delays could represent optimization efforts, perhaps to wait for a next generation of chips; new cooling or power solutions. 

There could be supply chain issues, such a shortage of crucial inputs. But we cannot ignore continuing optimization efforts that enable AI processes with lower energy consumption, computing intensity or infrastructure footprint. 

And capital efficiency also matters. The whole generative AI field moves rapidly and it also is conceivable that supply decisions incorporate a different mix of “build versus buy” choices, such as obtaining capacity by renting from third parties or partners such as OpenAI, which also is committed to building AI infrastructure. 

Business strategy might also play a role. Perhaps much of the early thinking by Microsoft was driven by support for OpenAI workloads. Some observers believe Microsoft’s expectations about the level of OpenAI computing support needs have lessened. 

That does not necessarily mean a shift in a primary reliance on “owned” facilities, as also is true for Google Cloud and AWS. 

Also, some might question whether it really is feasible for any of those firms to invest as much as they have claimed, given the growing amount of concern about the actual payback from such investments. 

But Alphabet recently reiterated its estimate of about $75 billion in 2025 capital investment on data centers and AI infrastructure. And despite reported pullbacks by Microsoft, many expect company data center and AI infrastructure capex in the $55 billion to $70 billion range, while Microsoft itself earlier in 2025 suggested capex spending in the $80 billion range.  

But by April 2025 the company had announced some project cancellations or delays. On the other hand, there also is some expectation that the firm could still spend close to $80 billion in 2025, as the firm itself said in late March. 

The point is that AI infrastructure investment in 2025 might still be more robust than some doubters have said would be the case.

And that might well wind up affecting investor perceptions of the firms making the investments. As optimism about AI arguably led to investors bidding up prices of firms in the field, so recent concerns about profitability have almost certainly caused a drop in equity valuations. By the end of the year it remains possible we'll witness another surge of interest.

And it remains possible, and perhaps likely, that by the end of 2025 data center and AI infrastructure investments by Alphabet, Meta, Microsoft and AWS will indeed be at about the expected levels talked about at the beginning of the year.

Embodied AI (Robots) Likely Key to Some Onshoring of U.S. Manufacturing

It is hard to avoid the conclusion that artificial intelligence, in the the form of embodied robots, will be an important part of the business case if manufacturing facilities return to the United States as part of restoring policies and firm responses, jobs will indeed be created, but not at the scale of the pre-offshoring era, it is reasonable to predict. 

Automation, particularly robots using artificial intelligence, likely will handle much of the repetitive, labor-intensive work. 

Study/Source	Estimated Job Creation	Role of Automation	Key Insights
Forbes (2025)	Hyundai's $20 billion investment expected to create 1,500 jobs in Louisiana	Automation is a major factor in reducing traditional manufacturing jobs.	Manufacturing production is high, but job creation is modest due to automation replacing manual labor.
Davron (2024)	Reshoring creates advanced manufacturing jobs requiring higher skills and wages	Advanced manufacturing relies heavily on technology, reducing reliance on manual labor.	Reshoring fosters innovation but requires a skilled workforce trained for automated processes.
Business Insider (2024)	Reshoring could add $10 trillion to the economy over the next decade	High-tech sectors benefit most, integrating automation for efficiency.	Automation is central to reshoring efforts, enhancing productivity but limiting traditional job growth.
Christian Science Monitor (2025)	AI-driven factories may add slightly more jobs than they destroy	Factories integrate AI and robotics to increase efficiency and resilience.	Human roles shift toward managing robots and AI rather than performing manual tasks.
Shoplogix (2023)	Robotics create new roles like technicians and engineers but displace manual labor jobs	Robots perform repetitive tasks faster and more accurately than humans.	Upskilling is essential as traditional roles are replaced by tech-focused positions.
LinkedIn (2025)	Manufacturing output increased 15% since 2020, employment rose only 3%	Automation reduces factory jobs while creating opportunities in high-tech roles.	Reshoring with automation boosts productivity but limits job creation in traditional sectors.

Where an existing garment factory presently operating in Southeast Asia employs X workers, a repatriated operation in the United States might require perhaps 10 percent to 20 percent of those workers. Recall that the reason such facilities moved from U.S. domestic locations to off shore is precisely lower labor rates. 

Assuming higher U.S. wage rates, the only way the economics would work is if far fewer workers were required. 

Study/Analysis	Source	Key Findings on Job Increases	Automation Consideration	Date
Reshoring Initiative Report	Reshoring Initiative	Estimated 1.6 million jobs reshored from 2010-2022, with potential for more if trends continue.	Notes automation reduces job counts; 2022 data shows 364,000 jobs added, but robotics limits scale vs. past.	2022
Oxford Economics:  How Robots Change the World	Oxford Economics	Robots could displace 20 million global manufacturing jobs by 2030, but reshoring may add some back.	Predicts automation will cap U.S. job gains; historical 1.6 jobs lost per robot suggests fewer net gains.	2019
MIT: Robots and Jobs	Acemoglu and Restrepo (NBER)	Between 1990-2007, 1 robot per 1,000 workers reduced employment by 6 workers locally.	Automation in reshored facilities could mean 50-70% fewer jobs than offshored totals due to robot density.	2017
Ball State University:  Manufacturing Study	Center for Business and Economic Research	U.S. lost 5.8 million manufacturing jobs (1980-2016), mostly to productivity automation, not trade.	Suggests reshoring creates high-skill jobs (e.g., technicians), but far fewer than original low-skill positions.	2017
McKinsey Global Institute: Future of Work	McKinsey	Automation could displace 16-20 million U.S. jobs by 2030, but reshoring may offset some losses.	Highlights that returning facilities will lean on robots/AI, creating 10-20% of original job numbers.	2017
ITIF: Robotics and Production	Information Technology and Innovation Foundation	Robotics boosts productivity, potentially adding manufacturing jobs in developed nations.	Job growth tied to engineers/tech roles; traditional labor replaced by robots, reducing total job count.	2019
Brookings: Automation and Jobs	Brookings Institution	Automation offsets job losses from trade, but reshoring impact limited by tech adoption.	Estimates 100-150 jobs per large reshored factory, vs. 500-1,000 historically, due to automated processes.	2015, 2022