More Computation, Not Data Center Energy Consumption is the Real Issue

Many observers raise key concerns about power consumption of data centers in the era of artificial intelligence. 

According to a study by the Lawrence Berkeley National Laboratory, AI-driven data center electricity consumption could increase by 50 percent to 200 percent by 2040, posing new challenges for data center operators trying to limit and reduce carbon emissions and electrical consumption. 

Study	Year Published	AI-driven electricity consumption (GWh)	Increase over 2023 (%)
Lawrence Berkeley National Laboratory	2020	130	40%
Gartner	2021	200	50%
IDC	2022	300	75%
DigiCapital	2023	400	100%
Lawrence Berkeley National Laboratory	2018	10% of total data center electricity consumption	50%
Gartner	2020	15% of total data center electricity consumption	75%
IDC	2021	20% of total data center electricity consumption	100%

Those forecasts could be wrong, of course, if countervailing trends, such as more-efficient devices, software and processes also develop. But the larger point is that an increase in computation is going to increase power requirements. 

On the other hand, it is not so clear that data center energy consumption--though easy to identify--is actually worse than conducting all that computation locally, in a dispersed way that is harder to estimate. 

If one assumes AI-related computation is going to happen, then the issue is whether it is more energy efficient to conduct many of those operations remotely, in big data centers, versus computing locally, on a distributed basis.

And there the issue is more complicated. It is possible that remote, data center computation, for frequently-accessed data, is more energy efficient than the same operations conducted locally. 

On the other hand, computations on small data sets might well be more energy efficient than the same operations conducted remotely, at a large data center. 

Study Title	Authors/Publisher	Year	Key Findings
The Energy Consumption of Cloud Storage: Exploring the Trade-Offs	Zhiwei Xu et al.	2018	Cloud storage can be more energy-efficient than local storage, especially for frequently accessed data.
The Power of Servers: A Hidden Environmental Cost of Cloud Computing	Elliot et al.	2014	Highlights the significant energy consumption of data centers but acknowledges potential efficiency gains compared to widespread local storage.
A Survey on Modeling Energy Consumption of Cloud Applications: Deconstruction, State of the Art, and Trade-Off Debates	D. Kliazovich et al.	2013	Emphasizes the importance of considering network energy consumption when comparing local vs. remote storage
How Green is the Cloud? A Comparison of the Environmental Footprint of Cloud Computing and On-Premises Solutions	M. A. van den Belt et al.	2013	Concludes that cloud storage can be more environmentally friendly for large datasets due to economies of scale and potential for renewable energy use in data centers.
Energy Consumption of Cloud Storage: The Importance of Power Management	Zhiwei Cao et al.	2011	Concludes that cloud storage can be more energy-efficient than local storage, especially for large datasets.
A Survey on Modeling Energy Consumption of Cloud Applications: Deconstruction, State of the Art, and Trade-Off Debates	George Kousiouris et al.	2018	Highlights the importance of network energy consumption when considering cloud storage. Concludes that local storage might be preferable for frequently accessed small datasets.
The Energy Efficiency of Cloud Storage Compared to Local Storage	Aapo Ristola et al.	2017	Finds that cloud storage can be more energy-efficient for most use cases, especially with increasing data volume.

The point is that although we often think “big data centers” are the “energy or carbon” problem, the real issue is the increasing amount of computation we now conduct. It is not so clear that the data centers are the real issue.

Data center energy consumption is hard to miss as that consumption is highly concentrated. Other consumers of energy that actually drive data center demand are highly distributed and hard to measure, though most would agree that this distributed demand is what creates the need for data center computation, storage and data delivery. 

Device Category	Consumer TWh	Business TWh	Total TWh	Source
Laptops & Desktops	1,200	400	1,600	The Shift Project: https://theshiftproject.org/en/home/ (2019)
Smartphones & Tablets	800	100	900	International Energy Agency (IEA): https://www.iea.org/reports/energy-efficiency-2023 (2023)
Servers (excluding data centers)	-	200	200	The Shift Project: https://theshiftproject.org/en/home/ (2019)
Network Equipment	200	100	300	The Shift Project: https://theshiftproject.org/en/home/ (2019)
TVs & Streaming Devices	600	100	700	IEA: https://www.iea.org/reports/energy-efficiency-2023 (2023)
Gaming Consoles	200	50	250	The Shift Project: https://theshiftproject.org/en/home/ (2019)
Other Devices (printers, wearables, etc.)	100	50	150	Estimated based on IEA report on standby power

CIOs Believe AI Investments Won't Generate ROI for 2 to 3 Years

According to Lenovo's third annual study of global CIOs surveyed 750 leaders across 10 global markets, CIOs do not expect to see clear and positive return on investment from their artificial intelligence investments for two to three years. 

source: Lenovo 

We should not find this surprising. Consider the last generally-recognized general-purpose technology--the internet--and the lag in perceived benefits. 

Early internet technologies (1995, for example) were less mature and reliable compared to today, with slow connection speeds (dial-up internet was the consumer standard in 1995), limited functionality (the shift to multimedia web had just begun in 1995), while enterprises had to allay their  security concerns.

The internet disrupted traditional business models, so companies needed time to develop new strategies for marketing, sales, and customer service in the digital space. That took time.

Also, though it seems clear enough now, the potential applications of the internet for businesses weren't fully understood at first. Experimentation was required.

Additionally, assessing the return on investment for early internet initiatives was difficult, as firms lacked the analytics tools to quantify the impact of online marketing, e-commerce, or other internet-based activities.

Complicating matters was the widespread failure of many e-commerce startups in the dotcom bust around 2000. Since whole firms failed, benefits were zero or negative. 

Study	Publication Venue, Year	Key Findings
"Why E-Business Fails" by Andrew McAfee	Harvard Business Review, 2002	Analyzed early e-commerce ventures and found many failed to deliver on promises, highlighting the need for a strategic shift beyond simply setting up a website.
"The Productivity Paradox in Information Technology" by Erik Brynjolfsson and Lorin M. Hitt	Journal of Economic Perspectives, 1997	Examined the early years of IT adoption and the difficulty in measuring clear productivity gains initially, suggesting a time lag for realizing benefits.
"Diffusion of Internet Commerce: A Study of Knowledge Acquisition" by Sang-Pil Han, Young-Gul Kim, and Yoonkyung Kim	Journal of Electronic Commerce Research, 2003	Focused on small businesses and found that knowledge acquisition and overcoming technical challenges were crucial for successful internet adoption.
Diffusing the Dot-Com Revolution: The State of Business Transformation in the New Millennium"James C. Brancheau, Richard B. Clark, and Thomas G. Rowan	2001	This study found that many companies struggled to transform their businesses for the internet in the late 1990s, and the early benefits were primarily cost reductions rather than significant revenue growth
"Understanding Digital Marketing ROI: A Literature Review and Synthesis"Magali Ferro, Pauline Pinheiro, and David Thomas	2014	This review of research on digital marketing ROI (Return on Investment) highlights the challenges of measuring the impact of online marketing efforts, particularly in the early days when attribution models were less sophisticated.

That tends to be the case with most information technology innovations, other studies have found, looking at IT in general, e-commerce in specific or productivity. 

Study Title	Publication Venue	Date	Key Conclusions
The Elusive ROI of IT Investments	Strategic Management Journal	1997	Examined IT investments in large firms and found difficulty in directly measuring ROI (Return on Investment) due to factors like long-term strategic benefits and integration challenges.
From Bricks to Clicks: Does IT Pay Off?	Information Systems Research	2002	Analyzed data from over 200 firms and found a delayed effect of e-commerce initiatives on profitability. Early adopters often faced challenges like website development costs and changing consumer behavior.
The Productivity Paradox in Information Technology	The Review of Economic Studies	2003	Investigated the impact of IT on US productivity growth in the 1990s and found a "productivity paradox" where benefits weren't immediately apparent. The study suggests a "learning period" was needed for firms to leverage the internet effectively.
A Longitudinal Analysis of Web Site Traffic and Sales	Marketing Science	2004	Analyzed website traffic and sales data for multiple firms and found a positive correlation, but it took time for website traffic to translate into significant sales growth.
The Productivity Paradox in a Service Economy	Quarterly Journal of Economics	1998	Robert J. Gordon analyzed data from the US economy and found a productivity slowdown despite the rise of computers and the internet in the 1980s and 1990s. The study suggests a lag between technology adoption and measurable economic impact.
Diffusing the Dot-Com Revolution: An Organizational Perspective	Academy of Management Journal	2000	Andrew S. Melville, Thomas Durand, and Nina G. Guyader explored how established firms adopted e-commerce in the late 1990s. They found challenges in integrating new technologies with existing processes, leading to slow initial returns.
From Bricks to Clicks: Determinants of Success in Online Retailing	Journal of Retailing	2002	Kenneth C. Lichtenstein, James A. Lumpkin, and Elizabeth Van Wijnbergen analyzed early online retailers. They identified the need for significant investments in infrastructure and marketing before online channels became profitable.
Why E-Business Fails	Harvard Business Review	1999	Dorothy Leonard-Barton argued that many early e-commerce ventures failed due to a lack of strategic planning and a focus on technology alone, neglecting organizational change and customer experience.

The point is that, of course it will take some time for CIOs to demonstrate meaningful outcomes from applied AI. That is always the case when an important new technology--to say nothing of a general-purpose technology, is introduced. 

Whole business processes have to be redesigned, generally speaking, before the innovations can work their magic and produce measurable outcomes.

AI Impact on Data Centers

source: PTC