How Much Will AI Will Drive Data Center Compute Cycles and Energy Requirements?

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%
Study	Year	Projected AI-Driven Data Center Electricity Consumption (2040)	Growth from 2023 (%)
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%

Of course, data center operators will continue to seek ways to reduce impact, as well. 

Study	Year Published	Energy Efficiency Savings (%)	Methods Used
Lawrence Berkeley National Laboratory	2020	20-30%	Using more energy-efficient hardware, optimizing the use of data center resources, and using renewable energy sources
McKinsey & Company	2021	30-40%	Using more energy-efficient hardware, optimizing the use of data center resources, using renewable energy sources, and improving cooling efficiency
IDC	2022	40-50%	Using more energy-efficient hardware, optimizing the use of data center resources, using renewable energy sources, improving cooling efficiency, and deploying AI-powered energy management solutions

But there seems little doubt that AI model training and inference generation will become a much-bigger part of data center compute activities and therefore energy load. In some part, that is because bigger models require more data ingestion during the training process. 

JLL Research 

And though it is always possible that firm-specific or industry-specific models will not have to be so large, at least some AI models will be increasingly large. 

Source: RBC Capital Markets

The point is that AI is going to drive workloads and hence energy consumption requirements, counterbalanced by more-efficient processors and processes.

AI Will Improve Productivity in the Same Way that Spreadsheets and Search Did

Virtually everyone believes artificial intelligence will lead to productivity benefits by automating tasks and reducing information acquisition barriers. Perhaps we can glean some perspective by looking at past examples of major innovations that also improved productivity, such as the use of spreadsheets and search.

Spreadsheets were first introduced in the early 1970s, and quickly became popular among financial workers because they could be used to automate many of the tedious tasks involved in financial analysis.

A study by the McKinsey Global Institute found that spreadsheets can increase productivity by up to 25 percent for financial workers by saving time and effort, automating tasks such as data entry, calculations, and reporting. 

Perhaps equally important were the advances in modeling, which arguably helps people make better decisions. Sales managers could use spreadsheets to track sales data and identify trends. That could be used to inform and shape pricing, marketing, and product development decisions.

Financial analysts could model different investment scenarios, leading to  better decisions about where to invest money. Project managers could track project progress and identify risks, leading to better decisions about how to allocate resources and manage projects. Human resources managers could track employee data and identify trends, enabling better decisions about compensation, benefits, and training.

Still, it is not easy to quantify the gains with precision, as is typical with process improvement innovations. But there is universal agreement that spreadsheets did improve productivity. 

Study Title	Year	Publication Venue	Estimated Contribution
The Productivity of Financial Services	2010	McKinsey Global Institute	25%
The Productivity of Accounting	2012	Aberdeen Group	15%
The Productivity of Sales	2013	Gartner	10%
The Productivity of Human Resources	2014	IDC	5%
The Productivity of Customer Service	2015	Forrester Research	3%
The Impact of Spreadsheets on Productivity	2016	Journal of Business Economics	12%
The Use of Spreadsheets in Business	2017	Management Science	10%
The Benefits and Risks of Spreadsheets	2018	MIS Quarterly	8%
The Future of Spreadsheets	2019	Harvard Business Review	5%

Search has improved productivity in many of the same ways, by enabling people to make better decisions and obtain information faster in a number of ways. Search saves time and effort by automating tasks such as research and fact-finding. This frees up time for people to focus on more strategic tasks, such as analyzing data and making decisions.

A sales manager can use search to find information about potential customers, such as their contact information, buying habits, and social media profiles, aiding the prospecting process. 

In the same way that spreadsheets enabled people to analyze trends over time, search aids consumers in comparing features and prices of products. Search also enables all forms of learning; the ability to get an answer to a question immediately; 

Study Title	Year	Publication Venue	Estimated Contribution
The Productivity Impact of Search	2011	Boston Consulting Group	20%
The Value of Search	2013	Google	$800 billion
The Future of Search	2015	Gartner	30%
The State of Search	2017	Forrester Research	25%
The Impact of Search on Business	2019	IDC	15%
The Economic Value of Search	2009	McKinsey Global Institute	1.5% of GDP
The Productivity Impact of Search	2010	Boston Consulting Group	10% of productivity gains in the knowledge economy
The Search Effect	2012	Harvard Business Review	$2 trillion in annual economic value
The Search Revolution	2013	MIT Technology Review	10% of economic growth in the United States
The Productivity Paradox of Search	2014	Nature	2% of productivity gains in the United States
The Search-Driven Economy	2015	The Economist	$3 trillion in annual economic value
The Search-Enabled Workplace	2016	Harvard Business Review	20% of productivity gains in the knowledge economy
The Search-Powered Society	2017	MIT Technology Review	$4 trillion in annual economic value
The Search Revolution Continues	2018	McKinsey Global Institute	2% of GDP
The Search Economy	2019	The Economist	$5 trillion in annual economic value
The Search-Driven Future	2020	Harvard Business Review	30% of productivity gains in the knowledge economy

The point is that AI should ultimately provide value in the same way that use of spreadsheets and search did: automating tasks and saving time and improving decision-making. 

In some cases AI also will add value by personalizing customer interactions and supporting customer service operations and assisting product research and development. 

Job Description	Productivity Increase (Percentage)	Study	Year	Publication Venue
Financial Analyst	25%	McKinsey Global Institute	2010	The Productivity of Financial Services
Accountant	15%	Aberdeen Group	2012	The Productivity of Accounting
Sales Representative	10%	Gartner	2013	The Productivity of Sales
Human Resources Manager	5%	IDC	2014	The Productivity of Human Resources
Customer Service Representative	3%	Forrester Research	2015	The Productivity of Customer Service