In Steven Carlini’s view, data centers are approaching a tipping point: AI and digitalization are pushing facilities toward much greater power density, larger campuses, new cooling systems and closer coordination with electricity providers. That is the thesis of his Schneider Electric-sponsored DatacenterDynamics article, published October 5, 2026—not an independently established industry consensus. Carlini’s picture of the next 25 years is a forecast, and the article’s examples and market figures should be read as claims made in that sponsored piece.
What does Carlini mean by a data-center tipping point?
He argues that the industry is moving beyond incremental changes in facility size and equipment. The combination of digital services and AI workloads, in his account, is changing the scale of projects and the way operators must plan power, cooling, construction and automation. His conclusion is explicit: “But I believe we’ve reached a tipping point, and data centers will transform in dramatic ways over the next 25 years.”
The article’s clearest evidence for that argument is its account of facility scale. Carlini says a 3 MW data center was considered large in the 1980s, facilities of 5–20 MW were typical examples of large-scale development in the 1990s, and 25–100 MW facilities were considered extremely large in the 2000s and 2010s. He also cites 300 MW campus projects under construction and 1 GW campuses planned. Those latter examples are reports in the article, not an independently checked inventory of projects or proof that every planned campus will be built.
| Period or status | Scale described by Carlini | How to read it |
|---|---|---|
| 1980s | 3 MW described as a large data center | Historical comparison in Carlini’s article |
| 1990s | 5–20 MW facilities | Historical comparison in Carlini’s article |
| 2000s–2010s | 25–100 MW facilities described as extremely large | Historical comparison in Carlini’s article |
| Under construction, as reported in the article | 300 MW campus projects | Examples cited by Carlini; project status is not independently verified here |
| Planned, as reported in the article | 1 GW campuses | Plans cited by Carlini, not completed capacity |
Carlini also gives an estimate of 54 GW for the installed capacity of all data centers worldwide at the end of 2023. The article does not establish this as a verified global inventory, so it is best treated as his reported estimate rather than a definitive count.
Why does larger scale change power planning?
A campus demanding hundreds of megawatts cannot treat electricity sourcing as a background utility detail. Carlini expects operators to work more closely with utilities and to combine grid supply with tools such as renewable and carbon-free electricity procurement, virtual power purchase agreements (PPAs), renewable-energy credits, battery storage and, in some cases, on-site generation. These are approaches he anticipates or describes; the article does not rank them or establish which mix is right for a particular site.
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A PPA is a contractual arrangement for purchasing electricity, often associated with a renewable-energy project. Carlini says the PPA market has grown by an average of 33 percent annually since 2015. The article does not identify the original publisher or underlying dataset for that figure, so it should not be read as an independently validated growth rate.
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Why small modular reactors are not an immediate answer
Carlini cautions against treating small modular reactors (SMRs) as a near-term solution to data-center power demand. His article forecasts that most reactors under development would come online in 2035–2040. That timeline makes them a potential future option in his account, not a source of power that operators can assume will be available at scale now.
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More computing power concentrated in server racks raises heat-removal demands. Carlini expects liquid cooling to become more common as GPU-accelerated systems increase power density. He mentions direct-to-chip cooling, which removes heat at processors through a liquid loop, and immersion cooling, in which equipment is cooled in a liquid environment. The article describes these as growing approaches; it does not establish that either is universally preferable to air cooling.
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Cooling choices can involve a water-versus-electricity trade-off, as Carlini notes. The article does not quantify that trade-off for specific climates, facility designs or cooling systems, so it cannot support a universal claim that one approach always uses less water or energy. Site conditions and the workload matter, and the sponsored piece does not supply facility-level comparisons.
Carlini cites a liquid-cooling market forecast of USD 16.79 billion by 2031, with a nearly 25 percent compound annual growth rate (CAGR) from 2024 through 2031. He does not name the forecast’s original publisher in the article, so these are figures reported by Carlini, not independently confirmed market measurements.
What does the article predict about energy and building markets?
The article includes several market estimates to illustrate the scale of investment it associates with data-center transformation. The original publishers behind these figures are not named in the article; each should therefore be understood as an estimate or forecast reported by Carlini, not as a separately verified market fact.
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| Area | Figure reported by Carlini | Qualification |
|---|---|---|
| Diesel generator market | USD 1.1 billion in 2023 to USD 2.1 billion in 2032 | Market estimate and forecast as reported in the October 5, 2026 article |
| Smart energy market | USD 153.80 billion in 2022; projected 9.6 percent CAGR from 2023 through 2030 | Market estimate and forecast as reported by Carlini |
| Liquid-cooling market | USD 16.79 billion by 2031; nearly 25 percent CAGR from 2024 through 2031 | Market forecast as reported by Carlini |
| Green-buildings market | USD 565.33 billion in 2024 to USD 1,374.2 billion in 2034 | Market estimate and forecast as reported by Carlini |
| Hydrogen demand | Clean hydrogen forecast to account for 75–100 percent of demand by 2050 | Long-range forecast as reported by Carlini; not a data-center-specific demand figure |
These figures suggest the scope of the trends Carlini describes, but they do not show how much any one data center will spend or which technology will prevail. The article does not supply underlying market-study links, facility-level cost comparisons or independent confirmation for the estimates.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How much automation does Carlini expect by 2050?
For 2050, Carlini envisions AI-based optimization extending across cooling, workload movement, power systems and coordination with utilities. He also anticipates robots assisting with installation or maintenance. These are elements of his long-range vision, not guaranteed outcomes or a timetable established for the industry as a whole.
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The practical implication of this forecast is that data-center design may increasingly involve software and automation alongside physical infrastructure. But the article does not specify which tasks will be automated first, what levels of autonomy facilities will reach, or how quickly operators will adopt these systems.
What the sponsored article establishes—and what it does not
The article lays out a coherent forecast: growing facility scale would intensify power and cooling requirements, which in turn could reshape energy procurement, backup systems and operations. Its historical size comparisons and cited project examples illustrate that case. Its market figures add context, but the article does not name their original publishers, link to underlying datasets or independently validate them.
Quick Recap
Because the article is sponsored by Schneider Electric and written by its identified author, its predictions should be understood as a vendor-affiliated perspective. It is useful as a statement of Carlini’s view of likely infrastructure changes, not as proof that every forecast will happen or that the industry has reached a universally recognized tipping point.
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