Rethinking Concerns About
AI’s Energy Use
By Daniel Castro | January 29, 2024

Concerns about the energy used by digital technologies
are not new. Near the peak of the dot-com boom in the
1990s, a Forbes article lamented, “Somewhere in
America, a lump of coal is burned every time a book isordered online.” 1 The authors of the article, which became
widely cited in subsequent years in debates about energy
policy, estimated that “half of the electric grid will be
powering the digital-Internet economy within the next
decade.” 2 However, the estimate was wrong, with errors inboth its facts and methodology.3 In hindsight, there is nolonger any dispute, as the International Energy Agency(IEA) estimates that today’s data centers and datatransmission networks “each account for about 1–1.5% of
global electricity use.” 4This mistake was not an isolated event. Numerous headlines haveappeared over the years predicting that the digital economy’s energy
footprint will balloon out of control. 5 For example, as the streaming wars
kicked off in 2019—with Apple, Disney, HBO, and others announcing video
streaming subscription services to compete with Netflix, Amazon, andYouTube—multiple media outlets repeated claims from a French think tankthat “the emissions generated by watching 30 minutes of Netflix is thesame as driving almost 4 miles.” 6 But again, the estimate was completely
wrong (it is more like driving between 10 and 100 yards), resulting from a
mix of flawed assumptions and conversion errors, which the think tankeventually corrected a year later. 7
With the recent surge in interest in artificial intelligence (AI), people areonce again raising questions about the energy use of an emerging
technology. In this case, critics speculate that the rapid adoption of AI
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combined with an increase in the size of deep learning models will lead toa massive increase in energy use with a potentially devastating
environmental impact. 8 However, as with past technologies, many of theearly claims about the consumption of energy by AI have proven to be
inflated and misleading. This report provides an overview of the debate,
including some of the early missteps and how they have already shaped
the policy conversation, and sets the record straight about AI’s energy
footprint and how it will likely evolve in the coming years. It recommends
that policymakers address concerns about AI’s energy consumption bytaking the following steps:

Develop energy transparency standards for AI models.



Seek voluntary commitments on energy transparency forfoundation models.



Consider the unintended consequences of AI regulations onenergy use.



Use AI to decarbonize government operations.

THE FACTS ABOUT AI’S ENERGY USAGE AND CARBON EMISSIONSCreating accurate estimates of the energy use and carbon emissions of AI
systems over their lifetimes is challenging because these calculations
depend on many complex factors, including details about the chips, coolingsystems, data center design, software, workload, and energy sources used
for electricity generation. This problem is not unique to AI. As a group ofenergy researchers described the problem in an article in the Annual
Review of Energy and the Environment:
Creating credible estimates of electricity requirements for information
technology is fraught with difficulty. The underlying data are not knownwith precision, the empirical data are limited, the most useful data areoften proprietary, and the technology is changing so rapidly that evenaccurate data are quickly obsolete. 9

However, several studies have attempted to quantify the current and futureenergy demands and carbon emissions of AI systems. Unfortunately, someof the initial estimates have fallen into the same trap as past early studies
about the energy use of digital technologies and have produced misleading
estimates. These studies generally consider the energy needed for an AI
system over its lifetime in two stages: 1) training the AI model; and 2) using
the AI model to respond to specific queries—a process called “inference.”

Training AI Models
Researchers at the University of Massachusetts Amherst estimated in2019 the carbon emissions of several AI models, one of the first major
studies of its kind. 10 The study found that BERT—which at the time was
Google’s state-of-the-art large language model (LLM)—emitted
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approximately 1,438 pounds of carbon dioxide (CO2) during 79 hours of
training using 64 advanced graphics processing units (GPUs), the chips
commonly used for training AI models because of their superior parallel
processing capabilities. To put this in perspective, a roundtrip flight fromNew York to San Francisco creates approximately 2,000 pounds of CO2
emissions per passenger. The researchers also estimated carbon
emissions for training an AI model for neural architecture search (NAS), a
technique for automatically finding one or more neural networkarchitectures for a given task—one of the most computationally complex
problems in machine learning. Specifically, they evaluated the energy
usage of a NAS used to create a better English-German machinetranslation model. 11 The researchers estimated that training the model inquestion generated 626,155 pounds of CO2 emissions (roughly equivalent
to 300 roundtrip flights from the East Coast to the West Coast). 12
Not surprisingly, given journalistic tendencies to skew toward negative
coverage of tech, virtually all the headlines in the popular media focusedon this latter estimate despite its narrow use case. 13 Even respectedscientific news outlets such as MIT Technology Review ran such headlines
as “Training a single AI model can emit as much carbon as five cars in their
lifetimes.” 14 These articles suggested that the massive energy needed totrain this particular AI model was normal despite this estimate clearlyreferring to an atypical example. It would be like an automotive news outlet
running an article that suggested “driving a car emits as much carbon as
an airplane” based only on a study that looked at the environmental impactof a flying car prototype.
Moreover, both the original research paper and the subsequent news
articles often noted that while the large AI model outperformed existingones at language translation benchmarks, the improvements were only
marginal. The implication was that AI researchers are making trivial
performance improvements at the expense of non-trivial amounts ofcarbon emissions. Indeed, other AI researchers made this point explicit in a
widely read paper “On the Dangers of Stochastic Parrots: Can Language
Models Be Too Big?” 15 They argued that it is “environmental racism” forwealthy Western nations to deploy ever-larger AI models because these AI
systems will have negative impacts on poor communities in the Global
South. Specifically, they wrote:
Is it fair or just to ask, for example, that the residents of the Maldives(likely to be underwater by 2100) or the 800,000 people in Sudan
affected by drastic floods pay the environmental price of training anddeploying ever larger English [language models], when similar largescale models aren’t being produced for Dhivehi or Sudanese Arabic? 16

Given the charges—that training AI systems is not only dangerous for theenvironment but also an overt act of racism—it is not surprising that many
policymakers have raised questions about AI’s energy consumption.
However, the headline-making estimate in the 2019 study was wildly
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incorrect—just like many prior claims about the oversized energy footprintof digital technologies. The University of Massachusetts Amherst
researchers had made several false assumptions that grossly inflated theirestimates both for the total energy used and the carbon emissions. In
response to the 2019 study, the researchers involved in the NAS model
provided a detailed summary of the energy use and carbon emissions fromtheir work, noting why the outside researchers’ estimates were wrong. The
actual emissions were 88 times smaller than the earlier study’s estimate. 17
Unfortunately, the popular media paid little attention to correcting therecord or noting the new findings, and so the initial impressions have
lived on.
Researchers have published multiple studies in subsequent years
estimating the energy needed to train many well-known AI models as wellas their carbon emissions. As shown in table 1, while larger models
generally require more energy usage than smaller ones do, the exact
figures vary significantly across different AI models. For example,
researchers estimate that training GPT-3—the 175 billion parameter AI
model used in the popular ChatGPT application—created 552 tCO2
emissions, but comparable AI models including OPT (a 175 billion
parameter AI model created by Meta) and Gopher (a 280 billion parameterAI model created by Google) have significantly smaller carbon footprints.
Moreover, the efficiency of training AI models continues to improve. For
example, 18 months after GPT-3, Google produced GLaM, an LLM with 1.2
trillion parameters. Despite GLaM being nearly 7 times larger than GPT-3
and outperforming the other AI model, GLaM required 2.8 times lessenergy to train. 18 Finally, the energy mix used to power the data center
where developers train an AI model impacts its carbon emissions. For
example, the developers of BLOOM used a French data center powered by
nuclear energy, which reduced its carbon footprint. 19
Despite the new research, groups critical of AI have repeatedly cited theinitial incorrect study in their demands for policymakers to reduceinvestment in large-scale computing resources. For example, the American
Civil Liberties Union (ACLU) sent a letter to the Office of Science andTechnology Policy (OSTP) in October 2021 complaining about the“environmental costs” of the White House’s planned National AI Research
Resource (NAIRR) and arguing that “the NAIRR should focus on offering analternative to the data- and compute-hungry applications that are the focus
of many industry and research labs.” 20 Similarly, the Center for AI and
Digital Policy falsely claimed in 2022 that “AI-enabled systems require
exponentially rising computing power. This increase in computing power
requires substantial energy consumption, generating a huge carbon
footprint and upending the green effects of digitalization.” 21 In each case,they made these claims despite overwhelming evidence showing they weremisleading and overblown.

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Table 1: Estimated energy demand of training various AI models

Chips(model x #)Hours

Energy(MWh)

BERT

0.1B V100x64

79

1.5

0.7

Strubell etal., 2019 22

GPT-2

1.5B TPUv3x32

168

1.7*

0.7*

Strubell etal., 2019 23

Llama 2

7B A100x(n/a)

N/A

74*

31.2

Meta,
2023 24

Llama 213B A100x(n/a)

N/A

147*

62.4

Meta, 2023

Llama 270B A100x(n/a)

N/A

688*

291.4

Meta, 2023

LaMDA

137B TPUv3x1024

1,385

451

26

Thoppilan etal., 2022 25

GPT-3

175B V100x10000

355

1,287

552

Patterson etal., 2021 26

OPT

175B A100x992

N/A

N/A

75

Zhang et al.,2022 27BLOOM

176B A100x384

2,820*

433

24.7

Luccioni etal., 2022 28

Gopher

280B TPUv3x4096

920

1,151*

380

Rae et al,2022 29

PaLM

540B

TPUv4x6144

1200

TPUv4x3072

326

3,436*

271.4

Chowdery etal., 2022 30

GLaM

1,162B TPUv4x(n/a)

N/A

456

40

Patterson etal., 2022 31

GPT-4

1,800B A100x25000

2280

N/A

N/A

Walker,2023 32

Model

# of
Parameters

CO2
Emissions(metric
Estimate
tons)
Source* Inferred based on available data, see appendix for details

Using AI Models
Despite the attention from policymakers and the media on the energy costsof training AI models, multiple studies have concluded that most of the

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energy costs associated with AI systems come from using AI models—aprocess known as “inference” (because the model is inferring results
based on a given input). For example, Amazon Web Services estimates that
90 percent of the cost of an AI model comes from inference. 33 Similarly, a
study from Schneider Electric estimates that 80 percent of the AI workload
in data centers in 2023 is from inference and 20 percent is for training. 34
Finally, a study by researchers at Meta notes that the exact breakdown
between training versus inference varies across use cases. For LLMs, they
estimate that inference is associated with 65 percent of the carbon
footprint, but for recommendation models where parameters must be
updated frequently based on new data, they estimate an even split
between training and inference. 35
Multiple factors impact the amount of energy used during inference,
including the type of task and the AI model. As shown in table 2, the energy
requirements for inference can vary significantly by task. For example,
using an AI model to classify text is generally computationally less intensive(and thus uses less energy) than using AI to generate an image. 36 Different
AI models also have different energy costs, and within specific models(e.g., Llama 2 7B versus Llama 2 70B), a larger number of parameters
generally requires more energy for inference.

Table 2: Average energy use per 1,000 queries by task 37
Task

kWh

Text classification

0.002

Image classification

0.007

Object detection

0.038

Text generation

0.047

Summarization

0.049

Image generation

2.907

Given that training a particular AI model incurs a one-time cost, whereasusing an AI model continues to consume energy over time, it makes sensethat most of the energy used for AI will eventually come from inference. It
also means that the energy requirements for running AI models will have asignificant impact on the overall energy use for AI systems. While mostcritics have focused on the energy used to train AI models, some people
have expressed concern about the energy used during inference. 38 For
example, writing in the October 2023 edition of the journal Joule, one
researcher estimated that interacting with an LLM requires approximately

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10 times as much energy as conducting a typical web search query, andextrapolated from that estimate to conclude that “the worst-case scenario
suggests Google’s AI alone could consume as much electricity as a country
such as Ireland (29.3 TWh per year).” 39
There are many reasons to doubt that such a “worst-case” scenario is on
the near horizon. In 2022, Google’s total global energy consumption acrossthe entire company was 21.8 TWh. 40 For the worst-case prediction to betrue, Google’s energy use for AI alone would have to more than exceed its
current total global energy use. It is true that the company’s energy
consumption has grown over time, particularly from its data centers, as its
business has grown. For example, Google’s data centers used about 3 TWhmore electricity in 2022 than the year before. 41 But while its overall energy
usage has grown, for the three years between 2019 and 2021, theproportion of energy it used for machine learning remained constant—between 10 to 15 percent of its total energy consumption—with
approximately 60 percent of that used for inference. 42
One explanation for the relatively constant proportion of energy used for
inference is the improvements seen in AI models and hardware. Indeed, as
shown in table 3, both performance and efficiency tend to improve overtime. The table shows that over a few years, the accuracy of computer
vision AI models improved significantly. In addition, the energy
requirements for inference across these models generally decreased withthe release of a newer chip. As noted in one recent study of the energy
used for inference in AI models, “when a SOTA [state-of-the-art] model isreleased it usually has a huge number of FLOPs [floating point operations],
and therefore consumes a large amount of energy, but in a couple of years
there is a model with similar accuracy but with much lower number of
FLOPs.” 43 In other words, the newest AI models may not be particularly
efficient by design because researchers are focusing on performance
improvements, but over time, researchers will address efficiency.

Table 3: Energy consumption for inference of deep neuralnetworks used for computer vision for two different GPUs 44
P100,
V100,
released June released June
2016
2017
(Joules)(Joules)Year

Top-1 Accuracy(ImageNet)

AlexNet

2012

56.52

0.033

0.023

GoogLeNet

2014

69.77

0.077

0.055

Vgg16

2014

71.59

0.542

0.373

ResNet50

2015

75.30

0.179

0.132

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7WHAT AI ENERGY FORECASTS GET WRONG
One reason forecasts about future energy demands from AI are so high isthey use inaccurate or misleading measurements, as described previously.
Another reason is the forecasts ignore the practical economic andtechnical realities that come with widespread commercialization of AI.

The Energy Use of AI Is Limited by Economic Considerations
Many of the high-end estimates for AI energy use are impractical becauseof the costs involved. Buying more chips, building more data centers, andpowering those data centers is expensive. For example, as even the authorof the prediction that Google’s AI alone might consume 29.3 TWh annuallyadmitted, reaching this level would require a $100 billion investment inchips along with billions more in operating costs for the data center andelectricity. 45 Even large tech companies would find it unsustainable to pay
for such massive amounts of computing. Businesses are profit-seeking
enterprises and computing costs money; therefore, they are not going tooffer services for long that cost more to operate than they receive inrevenue. Either the energy costs for using AI will come down or how
companies deploy AI will be limited by cost factors.

The Rate of Performance Improvements in AI Will Decline Over TimeAI models have improved significantly in the past few years. For example,
OpenAI’s LLM model, GPT-4, released in March 2023, can pass many
popular exams designed for humans, such as the SAT, GRE, LSAT, and AP
tests for a variety of subjects. 46 These results are a substantial
improvement over its earlier model released the prior year. While AI stillcannot perform many tasks as well as humans, such as abstract
reasoning, now that some AI models perform so highly on many
benchmarks, there is substantially less opportunity for improvement incertain domains. As a result, many developers will likely focus more on
optimizing their AI models rather than squeezing out ever-smaller
improvements in accuracy because they will not receive a return on
investment for building and operating larger models.

Future Innovations Will Improve AI’s Energy Efficiency
The history of computing is one of continuous innovation, and these
innovations extend to energy efficiency. For example, over the past decade,demands on global data centers have increased substantially even as theenergy intensity of data centers has decreased by approximately 20
percent annually. 47 Between 2010 and 2018, there was a 550 percent
increase in compute instances and a 2,400 percent increase in storage
capacity in global data centers, but only a 6 percent increase in global data
center energy use. 48 These energy efficiency gains came from
improvements in hardware, virtualization, and data center design, and they
are part of the reason that cloud computing has been able to scale.

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Similar trends are already appearing in AI. As one recent paper notes,“Many studies report that the size of neural networks is growing
exponentially. However, this does not necessarily imply that the cost is also
growing exponentially, as more weights could be implemented with thesame amount of energy, mostly due to hardware specialization but
especially as the energy consumption per unit of compute is decreasing.” 49
Improvements in hardware and software will likely keep the pace of energy
growth from AI in check. Chipmakers continue to create more efficient
GPUs for AI. For example, Nvidia’s recent transition from one generation ofGPUs to another resulted not only in significantly faster processing but alsonearly doubled energy efficiency. 50 Likewise, researchers continue toexperiment with techniques, such as pruning, quantization, and distillation,
to create more compact AI models that are faster and more energy efficientwith minimal loss of accuracy. 51 These types of advancements are one
reason the proportion of energy Google uses for AI has remained constantin recent years despite machine learning growing to account for 70 to 80
percent of the compute used at the company. 52 Indeed, as one researcher
succinctly put it, “[AI’s] energy consumption is not skyrocketing, contrary tocommonly expressed fears.” 53

AI’s Energy Footprint Ignores Substitution Effects
Discussing the energy usage trends of AI systems can be misleading
without considering the substitution effects of the technology. Many digitaltechnologies help decarbonize the economy by substituting moving bits formoving atoms. For example, sending an email replaces mailing a letter,streaming a movie replaces renting a DVD, and participating in a videoconference replaces traveling to an in-person meeting. AI will have a similar
impact over time, both by further digitalizing many activities (such as byimproving the quality of video calls) and by using AI to complete tasks more
efficiently than using human labor.
One study in 2023 estimates the carbon footprint of using AI versus using
a human for writing a page of text or creating an illustration. Afterconsidering the carbon emissions for different AI models (ChatGPT,BLOOM, Midjourney, and DALLE-2) and comparing workers in the UnitedStates and India, the researchers found “AI writing a page of text emits
130 to 1,500 times less CO2e than a human doing so” and “AI creating animage emits 310 to 2,900 times less.” 54 Of course, since using AI does not
eliminate humans—they still exist and eat, breathe, etc.—using AI does not
eliminate these carbon emissions. But AI does eliminate the carbon
emissions from the devices humans use for these tasks, such as laptop or
desktop computers. As shown in table 4, these savings can be substantial;
however, there are limits to generalizing these findings. For example, by
making it easier to produce text and images, the volume of activity may
increase. Nevertheless, these findings show how, holding all else equal,
using AI to substitute for human labor can reduce carbon emissions incertain cases.

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Table 4: Carbon footprint (grams CO2e) for using a human (in the
United States) versus AI (BLOOM/Midjourney) for certain tasksAI

LaptopDesktop

Human

Writing a page of text

0.95

27

72

1,400

Creating an image

1.90

100

280

5,500

HOW AI’S ENERGY USE FITS INTO THE BIGGER PICTURE
The debate about AI’s energy use is part of a larger debate about how toaddress global climate change. Within that context, there are importantfactors policymakers should keep in mind.

AI Will Play an Important Role in Addressing Climate Change
There are many opportunities to use AI to reduce carbon emissions,
support clean energy technologies, and address climate change. These
opportunities span multiple industries, including the transportation,
agriculture, and energy sectors. For example, AI is crucial for integrating
renewable energy sources such as wind and solar into the electric grid byusing data points to forecast supply and demand. Likewise, utilities areusing AI for predictive maintenance of energy assets, managing andcontrolling grids, and setting dynamic pricing—all critical elements for an
efficient electric grid. 55
AI can also help make sense of complex climate data from sensors andsatellites, such as changing sea levels, surface temperatures, and rainfall,to create better forecasts and address risks of climate change. For
example, AI can detect methane emissions from satellite data, allowing
regulators to more effectively monitor industry. 56 Similarly, farmers can use
AI for precision agriculture, reducing their use of fertilizer and water andtheir associated environmental costs. 57
Already businesses, governments, and consumers are using AI to operate
more efficiently. AI is a key part of creating smart cities that use AI to
operate efficient buildings, roads, waterways, and more. 58 For example, in
California, the government is using AI to monitor over a thousand cameras
to detect and respond to wildfires quickly, reducing carbon emissions thatcome from these fires. 59 And AI can enable firms to optimize industrial
processes, reduce waste, and use energy more efficiently, thereby reducing
their carbon intensity. 60 For example, logistics providers use AI to optimize
delivery routes, thereby reducing fuel consumption of their fleets. 61 And in
the consumer space, tools such as the Nest smart thermometers saved
customers 113 billion KWh between 2011 and 2022 and more efficientdriving from Google Maps has reduced carbon emissions by 1.2 million
metric tons. 62

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10As AI matures, policymakers should continue to look to the technology as a
key tool for addressing climate change.

There Is No Unique Market Failure for AI’s Energy Use
Solving the global climate challenge will require transitioning to clean
energy technologies that have a price and performance on par with dirty
ones. 63 In the interim, any activity that uses energy has an environmental
impact, and AI’s use of energy is no different. However, there are no uniquemarket failures associated with AI’s use of energy that would lead togreater environmental impact than alternative uses would. A kilowatt-hour
used for AI is no different than a kilowatt-hour used for watching television,
microwaving popcorn, powering lights, or any other activity. Indeed, as
noted previously, in many cases, AI applications will be used as a
substitute for less energy-efficient activities and to address climatechange. In both cases, those who consume energy must pay for it, andrational actors will generally seek to minimize these costs. While suchcosts may not include negative externalities associated with energy use,that problem is not unique to AI and cannot be addressed for AI alone.

Large Tech Companies Have Made Bold Net-Zero Commitments
Large tech companies are at the forefront of AI, and these are the same
companies that have made some of the boldest commitments amongcorporations to reducing their carbon footprints. Consider the following:

Google (now Alphabet) became carbon neutral in 2007, the firstmajor company to do so. 64 A decade later, it became the first major
company to purchase enough renewable energy to match its
electricity consumption. 65 And, in 2020, it purchased carbon
offsets to eliminate the company’s entire carbon legacy. 66 The
company continues to press forward and has committed tooperating all its data centers and campuses on carbon-free energy
by 2030. 67

Amazon co-founded The Climate Pledge in 2019, wherebycompanies commit to net-zero carbon emissions by 2040, 10 years
ahead of the Paris Agreement. 68 And, in 2022, Amazon reported
that 90 percent of the electricity it consumed came from renewablesources, and it was on track to reach 100 percent by 2025, five
years ahead of its goal of 2030. 69

Microsoft has committed to be carbon negative by 2030 andeliminate its company’s entire carbon legacy by 2050. 70 It has also
committed to using 100 percent renewable energy by 2025. 71

Facebook (now Meta) reached zero carbon emissions in its direct
operations (i.e., data centers and offices) in 2020 and has pledged
to reach net-zero emissions across its entire value chain by
2030. 72
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These companies have remained publicly committed to these pledges evenas they lead in the development and deployment of AI. Indeed, their netzero pledges are one of the reasons these companies must carefully
consider the efficiency of the AI models they train and deploy.

HOW POLICYMAKERS SHOULD ADDRESS AI’S ENERGY USE
Witnessing the rapid advancements in AI, policymakers around the world
are considering whether and how they should regulate the technology,
including its energy usage. For example, UNESCO’s “Recommendation on
the Ethics of AI”—which was adopted by 193 member states in November
2021—states, “Member States and business enterprises should assess the
direct and indirect environmental impact throughout the AI system life
cycle, including, but not limited to, its carbon footprint, energy consumption… and reduce the environmental impact of AI systems and data
infrastructures.” 73 The impact of AI on energy and the environment shouldbe part of the policy debate, but policymakers should also be careful not tooverreact, especially given the prevalence of misleading narratives falsely
depicting AI’s energy consumption as out of control.
There are reasonable steps policymakers can take to ensure AI is part ofthe solution, not part of the problem, when it comes to the environment. To
that end, policymakers should do the following:

Develop Energy Transparency Standards for AI Models
It is usually easier to manage things that can be measured, and energy useof AI models is no different. While many AI developers have begun
publishing model cards—short documents that accompany the release of
an AI model that detail information about its performance, limitations, andother relevant information—these do not always contain information about
the energy used to train or use them. 74 When they do include information
about the environmental impact, they tend to focus on the carbon
emissions from training rather than the energy needs for inference. 75 Thisfocus on the energy used for training is partially out of necessity, as thedevelopers of a model cannot control the hardware others might run theirmodel on in the future or specific use cases. But the amount of energy
used for training does not necessarily impact the energy that will be
required for inference. Therefore, choosing models based on the amount ofenergy used to train them rather than the life cycle energy costs could lead
to less-efficient outcomes.
To address this problem, policymakers should support the development ofenergy transparency standards for AI models, both for training andinference. In the United States, for example, the National Institute ofStandards and Technology should work with the Department of Energy todevelop a recommended best practice for assessing the training andinference energy costs. For example, this standard might include a set ofbenchmark tests and hardware to give comparable energy performance

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metrics across different models. The United States should also work with
the G7 and the Organization for Economic Cooperation and Development(OECD) to ensure broad adoption of these energy transparency standards
to avoid different disclosure practices in different jurisdictions, especiallysince some countries might make such disclosures mandatory.

Seek Voluntary Commitments on Energy Transparency forFoundation Models
While developing transparency standards for AI models will help, it will also
be important for leading AI companies to adopt these standards anddisclose this information publicly. The White House has proactively sought
out and obtained voluntary commitments from most of the leading U.S.based AI companies to promote “safe, secure, and transparent
development and use of generative AI (foundation) model technology.” 76
While these commitments included important pledges from companies toengage in extensive testing to detect vulnerabilities and promises to avoid
discrimination and bias in their models, they did not include any
commitments around energy. To address that shortcoming, the WhiteHouse should continue its dialogue with these companies to seek avoluntary commitment to publicly disclose the energy required to train andoperate these foundation models, as well as the associated carbon
emissions, especially for cloud-based AI service providers. Making thisinformation publicly available will give users of foundation models theoption to take the environmental footprint of AI into consideration when
deciding which AI services to use.

Consider Unintended Consequences of AI Regulations on Energy Use
Many policymakers have called on developers to ensure their AI models
minimize bias, avoid hate speech, limit disclosure of private information,
and align to other, often worthwhile, goals. In many cases, developers areactively working to create models and build safeguards to address theseconcerns because they have strong market incentives to do so. However,policymakers rarely consider that their demands can raise the energy
requirements to train and use AI models. For example, debiasing
techniques for LLMs frequently add more energy costs in the training andfine-tuning stages. 77 Similarly, implementing safeguards to check that
LLMs do not return harmful output, such as offensive speech, can result in
additional computing costs during inference. 78 Thus, many of the proposedmandates for AI models could come at the expense of energy efficiency
goals. The converse is also true: Mandates for energy-efficient AI models
could create trade-offs that result in AI models that are less fair and morebiased than they otherwise might be.
The point is not that policymakers should never regulate any AI system, butrather that they should avoid rushing to regulate until they fully understandthe implications of their decisions. For example, the EU’s AI Act initially
included no requirements around energy efficiency. However, in response
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to some of the misleading claims about AI’s environmental impact, theEuropean Parliament’s proposed revisions to the legislation included
substantial additions around energy, such as directing AI developers tointegrate “state-of-the art methods and relevant applicable standards toreduce the energy use, resource use and waste, as well as to increase theirenergy efficiency and the overall efficiency of the system.” 79 These
requirements were in tension with other obligations in the AI Act toeliminate bias from AI models. While the AI Act now only includes morereasonable energy transparency requirements, the proposal from theEuropean Parliament shows the potential for bad facts to lead to bad
policy.

Use AI to Decarbonize Government Operations
AI offers important opportunities to improve the quality and efficiency ofmany government services, and adopting AI broadly across government
agencies at every level should be a key priority for policymakers. In
addition, AI can help the public sector reduce carbon emissions throughmore efficient digital services, smart cities and buildings, intelligent
transportation systems, and other AI-enabled efficiencies. At the 2022
United Nations Climate Change Conference of the Parties (COP27), theUnited States launched the Net-Zero Government Initiative, which commits
national governments to reaching net-zero carbon emissions for theiroperations by 2050. 80 To accelerate the use of AI across government
agencies toward this goal, the president should sign an executive orderdirecting the Technology Modernization Fund—a relatively new funding
system for federal government IT projects—to include environmental
impact as one of the core priority investment areas for projects to fund. In
addition, the United States should invite and share best practices for using
AI in government from the other countries that are part of the Net-Zero
Government Initiative.

CONCLUSION
Policymakers need accurate information about the energy implications of
AI. Unfortunately, groups that oppose AI, whether from honest
misunderstanding of the evidence or intentional cherry-picking of the facts,
continue to push the narrative that AI’s energy footprint is growing out ofcontrol. In December 2023—more than two years after the record wascorrected—a columnist writing in The Guardian repeated the original false
and misleading statistic about AI’s energy impact that has generated somuch concern. The article stated:
A study in 2019, for example, estimated the carbon footprint oftraining a single early large language model (LLM) such as GPT-2 atabout 300,000kg of CO2 emissions—the equivalent of 125 round-trip
flights between New York and Beijing. Since then, models havebecome exponentially bigger and their training footprints will therefore
be proportionately larger. 81

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Just as the early predictions about the energy footprints of e-commerceand video streaming ultimately proved to be exaggerated, so too will thoseestimates about AI likely be wrong. But given the enormous opportunities
to use AI to benefit the economy and society—including transitioning to a
low-carbon future—it is imperative that policymakers and the media do a
better job of vetting the claims they entertain about AI’s environmental
impact.

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Conversions and inferences shown for Table 1. All figures from source citedin table, unless otherwise noted.
BERT
0.65 metric tons CO2e = 1,438 lbs
GPT-2
1.7 MWh = 32 GPUs x 168 hours x 289W* x 1.1 PUE*
*Using measurements from LaMDA. See Thoppilan et al., 2022.
0.7 metric tons CO2e = 1.7 MWh x 0.429 CO2e/KWh** Using measurement of U.S. average data center net CO2e/KWh from
Patterson et al., 2021.
Llama 274 MWh = 184,320 GPU hours x 400W
147 MWh = 368,640 GPU hours x 400W
688 MWh = 1,720,320 GPU hours x 400W
BLOOM
2,820 hours = 1,082,990 million GPU hours / 384 GPUs
Gopher1,151 MWh = 4,096 GPUs x 920 hours x 283W x 1.08 PUE
PaLM
3,436 MWh = [(6,144 GPUs x 1,200 hours) + (3,072 GPUs x 336 hours)] x
378.5W x 1.08 PUE

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ABOUT THE AUTHOR
Daniel Castro is the director of the Center for Data Innovation and vicepresident of the Information Technology and Innovation Foundation.
He has a B.S. in foreign service from Georgetown University and an
M.S. in information security technology and management from
Carnegie Mellon University.

ABOUT THE CENTER FOR DATA INNOVATIONThe Center for Data Innovation studies the intersection of data,technology, and public policy. With staff in Washington, London, andBrussels, the Center formulates and promotes pragmatic public
policies designed to maximize the benefits of data-driven innovation in
the public and private sectors. It educates policymakers and the public
about the opportunities and challenges associated with data, as wellas technology trends such as open data, artificial intelligence, and theInternet of Things. The Center is part of the Information Technology
and Innovation Foundation (ITIF), a nonprofit, nonpartisan think tank.

Contact: info@datainnovation.org
datainnovation.org

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