The research, co-authored by the Jack Kemp Foundation and Capital Policy Analytics, suggested that US consumers and small businesses could see a 70% uptick in their energy rates on the back of data center power consumption.
Already struggling against inflationary pressures, small businesses could be forced to scale back operations, reduce employee headcount, or cease operations entirely, co-author of the study Ike Brannon said.
Brannon added that the average US household could be paying over a thousand dollars more annually for electricity by 2030 if steps are not taken to resolve the energy issues created by data centers.
“As data center expansion accelerates, consumers and small businesses are likely to bear the brunt of the consequences through higher electricity costs and brownouts and blackouts across the country,” Brannon said.
Expectedly, AI is the driving force behind the rate increase suggested in the research. The report stated that the likely acceleration of data center growth over the next decade will be driven by the rapid adoption of the technology.
Other industry research reflects similar trends, with a Gartner report recently predicting that two-fifths (40%) of data centers will face constraints in power availability by 2027 on the back of AI.
In Europe, it’s a similar picture - McKinsey found that data center energy demands are set to triple by 2030 because of AI, with the power needed to fuel European sites set to grow from 62 terawatt-hours (TWh) to more than 150 TWh.
The report detailed a set of policy recommendations that could remediate the issues. Policymakers need to take immediate steps to protect consumers and smaller businesses from the effects of rising energy demand, Brannon said.
AI companies should be required to bear the costs of the additional energy they consume, the report added, to ease the burden on consumers and small businesses. This could mean charging data centers higher fees.
States in the US should also constrain subsidies on data center construction. Data center projects create few jobs and have little “local economic spillover effects,” the report argued.
Utility regulators could also reduce rates for data centers that implement significant energy-saving measures, thereby incentivizing sustainable practices and reducing their tax burden.
Outside the remit of policy action, other experts suggested that data center developers or constructors have measures available to them that could reduce their energy consumption.
“Leveraging solutions like battery energy storage systems (BESS) and exploring alternative energy sources like solar, wind, and nuclear, give data centres a path forward that allows for continued technical innovation while also promoting the clean energy transition," Himanshu Khurana, CTO at Powin, told ITPro.
George Fitzmaurice is a staff writer at ITPro, ChannelPro, and CloudPro, with a particular interest in AI regulation, data legislation, and market development. After graduating from the University of Oxford with a degree in English Language and Literature, he undertook an internship at the New Statesman before starting at ITPro. Outside of the office, George is both an aspiring musician and an avid reader.
Canalys Forums EMEA 2024 Organizations are being forced to rethink where they host workloads in response to ballooning AI demands combined with rising energy bills, and shoving them into the public cloud may not be the answer.
CIOs are facing a quandary over rising power consumption from the huge compute demands of training and deploying advanced AI models, while energy costs are simultaneously rising. Finding some way to square this circle is becoming a big concern for large corporates, according to Canalys.
Speaking at the recent Canalys Forum EMEA in Berlin, chief analyst Alastair Edwards said that every company is trying to figure out what model or IT architecture they need to deploy to take best advantage of the business transformation that "AI promises".
The public cloud vendors position themselves as the destination of choice for training AI workloads, and they certainly have the infrastructure resources, with capital expenditure on AI-capable servers up by about 30 percent this year, by some estimates.
But as an organization starts to look beyond training to putting those models to work – fine-tuning and inferencing with them – the question arises of how and where to deploy them in a scalable way, according to Edwards.
"The public cloud, as you start to deploy these use cases we''re all focused on and start to scale that, if you''re doing that in the public cloud, it becomes unsustainable from a cost perspective," he claimed.
But what is the alternative to cloud? Businesses have been migrating workloads to the cloud for a decade or so now because of the hassle of managing complex infrastructure, among other reasons, and many have downsized their own bit barns in response.
"Almost no organization these days wants to build their own on-prem datacenter," Edwards said. "They want to have the control, the sovereignty, the security, and compliance, but they want to locate it where they don''t have to deal with an increased power requirement, increased need for liquid cooling, which you can''t just repurpose an existing datacenter for."
"We''re seeing new business models emerging, companies which have invested in GPU capacity and are now developing GPU-as-a-service models to help customers access this. Whether that''s a sustainable model or not is debatable, but essentially, every customer needs help to actually define what that looks like," Edwards explained.
But whichever way you access it, infrastructure to drive AI looks like being a growth area for investment in the near future, according to the latest forecast from market watcher IDC. It estimates that corporates increased spending on compute and storage hardware for AI deployments by 37 percent in the first half of 2024, and forecasts that this will expand to top $100 billion by 2028.
Given that Microsoft alone has recently announced plans to raise $100 billion to invest in datacenters and AWS plans to spend $10.4 billion just on facilities in the UK, this figure seems like something of a conservative estimate.
IDC says that AI-enabled systems deployed in cloud and shared environments accounted for 65 percent of the entire server spend on AI during the first half of 2024 as hyperscalers, cloud service providers, and digital service providers built out their capabilities. In contrast, enterprises have largely lagged behind in adopting on-prem AI kit.
This matches what analyst firm Omdia has been telling us: that server demand for AI training is largely driven by a relatively small number of hyperscalers, and expenditure on servers is growing rapidly because AI calls for high-performance systems rammed with costly GPU accelerators.
Not surprisingly, the US leads the way in global AI infrastructure, according to IDC, accounting for almost half of the total spending in 1H24. It was followed by China with 23 percent, then the Asia-Pacific region on 16 percent, and EMEA trailing on just 10 percent.
Over the next five years, however, IDC expects Asia-Pacific to grow the fastest with a compound annual growth rate (CAGR) of 20 percent, followed by the US. Accelerated servers are forecast to make up 56 percent of the total market spending by 2028.
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