Podcast episode: The relationship between energy and AI is evolving rapidly

Welcome back to the IA's podcast, Everything Energy. I'm Dan Huitt. This

Everything Energy. I'm Dan Huitt. This

week, we're looking at what artificial intelligence means for the energy sector in 2026.

Sometimes to understand the world around us, you have to follow the money. And

right now, the money is heading towards AI. The sums are staggering. The capital

AI. The sums are staggering. The capital

expenditure of just five tech companies now exceeds global investment in oil and natural gas production. Last year, that figure stood at $400 billion, and this

year, it's expected to grow by 75%.

So, the money is clearly there for AI.

But is the energy sector ready for what comes next? In April 2025, the IEA

comes next? In April 2025, the IEA published a landmark analysis on the relationship between energy and AI. But

the field is evolving so rapidly that new data has come to light and new questions need answering. Here to help us understand the situation right now

are the lead authors of a new follow-up report, Thomas Spencer and Sid Singh.

Okay, Sid Singh and Thomas Spencer, thanks for thanks for joining us today.

Now, this is our second podcast on the relationship between energy and AI. So,

I think it's just worth reminding ourselves about the initial aims of the report and also why we're doing another podcast, why you've done another report

so soon after the last one.

Yeah, great to be with you, Dan. And um

it's a good starting question. You know,

at the IIA, we have report series that we do every year, like the world energy outlook, but typically when we have thematic reports, they're more one-off

and we don't revisit the topic uh so soon uh afterwards. But in the case of AI, things are moving so quickly that we felt it was an important um role for the

IEA. You know, we pride ourselves on

IEA. You know, we pride ourselves on being ahead of the curve. uh an

important role for us to come back and look again at this issue and see what has changed since uh the last time uh that we picked up on this topic in April last year. I mean just to give you a

last year. I mean just to give you a couple of examples a year ago a typical AI use case might have been someone writing a text query into a chatbot. Now

people are using AI for coding. AI is

running long duration tasks in the background while people do something else, work on something else, come back two hours later and the task is is finished. So the field itself is is

finished. So the field itself is is really moving incredibly quickly. Uh and

that of course has consequences for the energy sector which we wanted to look at in this report. Okay. Now let's start by unpacking some of the the big themes, the big issues surrounding energy and AI

and think about whether they've changed or not. So Sid, I want to ask you about

or not. So Sid, I want to ask you about investment because one of the ways to measure the expansion of AI is to look at investment trends and there are some

extraordinary sums in this report. So

talk us through them.

Uh sure Dan. So the technology sector is witnessing a historical buildout of physical infrastructure to cater to this

uh boom of AI. So AI models uh AI inference which means the use of AI uh by people uh the computations behind it takes place in data centers and so

that's what we track and we find that the largest technology companies on earth so what are called the hyperscalers and a couple of others are

spending uh over 700 billion US in 2026 on their capital expenditure. This of

course includes data centers but also includes a couple of other things. But

data centers are the primary driver of it. It was as high as $400 billion last

it. It was as high as $400 billion last year. So this number has been increasing

year. So this number has been increasing rapidly and it's been driven by data centers. In fact, we did a little back

centers. In fact, we did a little back of the envelope calculation and the total amount that was spent on the Apollo space program. This was a 10-year

long investment that was made by NASA that put humans on the moon.

that program cost less than the amount that was spent last year on data centers. So that's the volume of

centers. So that's the volume of investments that we're talking about here.

All right. Now, let's think about the efficiency of AI and how it's changed as opposed to this time last year. So, how

much energy does each AI query use today? And has it progressed in the way

today? And has it progressed in the way that you thought it would?

There's no easy answer to this to this question and this is because AI is not like apples or oranges. It's not a single good right there's different uh

services that we call uh AI. I think the simplest answer to the question though is to say what does a simple text query that's you know what people are usually

doing today when they think about AI they're asking chat GPT a question what does this cost in terms of energy consumption and the answer is surprisingly little we estimate that it

costs about 0.3 W hours to ask a model like chat GPT a simple text generation query uh for people who don't immediately have an idea of what that

means. It's about the same amount of

means. It's about the same amount of energy as running a TV for the same amount of time that it takes chat GBT to return your answer. That number has been

improving extremely rapidly. So, uh we say in the report that efficiency improvements in AI are moving faster than any other energy technology in

history. And this is essentially

history. And this is essentially happening in two ways. Uh first of all the hardware so the chips that AI models are trained and run on are getting more

and more efficient. We estimate by 50 to 60% per year. That's incredible. But

also the models themselves are getting more efficient maybe by a factor of 2 to 10 times per year. There's a wide range here because we don't have a precise data but certainly the leaps are are

impressive.

However, I said at the beginning, you know, there's no single thing that is called AI. It's a variety of services.

called AI. It's a variety of services.

And some AI services are much much more energy intensive. For example, video

energy intensive. For example, video generation can be an order of magnitude more energy intensive than than text generation. And some of these long

generation. And some of these long duration what are called agentic AI services. So

a model running in the background doing something for you processing a data set or scraping the web they can be maybe 100 to a thousand times more energy

intensive than simple text generation.

So what we have seen is incredible efficiency improvements but at the same time the development of more and more energyintensive AI modalities that are

increasingly being used by people in there everyday and that's why we project that energy demand for AI is going to continue to increase. Okay. So where

does that leave the energy footprint of AI right now? Is it where we thought it was going to be this time last year?

Last year uh about 500 terowatt hours of electricity was consumed by all data centers globally. Right? That's about

centers globally. Right? That's about

1.5% of all electricity consumption globally. Uh now that might not sound

globally. Uh now that might not sound like a lot but in fact data centers tend to cluster. That is they tend to emerge

to cluster. That is they tend to emerge around each other and they often tend to emerge around cities which are already big consumers of electricity. And so in those particular regions it can be as

high as onethird of the total electricity consumption of that particular city or that particular state which is quite significant. But what's

also interesting is uh what the subset of data centers that are actually dedicated to AI what's happening in that

space. So for example uh of the new AI

space. So for example uh of the new AI dedicated server racks. So think of a rack uh about the size of a refrigerator

and it contains uh you know the the computational equipment that helps train models and also use these models. This

single server stack uh is on track to consume as much electricity as about 65 households as early as next year. So

this is these are really you know small pieces of infrastructure that are consuming a lot of electricity very quickly. They also generate heat.

quickly. They also generate heat.

they're as heavy as an SUV truck. So, uh

it's the subset of data centers that deal with AI that is currently driving electricity consumption growth and uh even into the future that is where the big chunk of the of the growth comes

from.

Just remind us uh if that energy footprint is progressing in the way that we thought it was when we put out the report last year.

Indeed it is. uh you know I I think we are pretty pretty proud of the fact that our updated numbers remain in on track with what we had uh initially estimated

in the in the first report. So we find that uh in uh 2025 electricity consumption from data centers as a whole grew at 17%.

Which is much faster than the 3% of the total global overall electricity consumption growth. So it's it's much

consumption growth. So it's it's much faster uh than what everything else is growing. Uh but also within it, it's the

growing. Uh but also within it, it's the AI specific data centers that's driving that growth. In fact, these AI data

that growth. In fact, these AI data centers uh saw their electricity consumption growth growing by 50% in this uh one-year period.

Okay. Now, let's think about the the bottlenecks surrounding the expansion and roll out of AI. Are the bottlenecks roughly the ones that we thought we knew

about or have there been some new ones that we've spotted really to do with the scale of it and the amount of money that's been put into it right now?

Bottlenecks that we saw coming last year have if anything gotten worse uh and new ones have arisen. So last year we

identified for example transformers and gas turbines uh as bottlenecks within the energy sector. Data centers require transformers to be connected to the grid

and uh many of them are powered by natural gas which requires uh gas turbines if new power plants are to be built. So last year for example we saw

built. So last year for example we saw global orders for natural gas turbines increase by an astonishing 70%.

uh that's uh puts total orders in 2025 at the highest ever level since the year 2000. If you order a gas turbine now,

2000. If you order a gas turbine now, you have to wait 5 to 6 years to receive uh the equipment. In terms of you know grid connection timelines uh and the

time that it takes to build uh new uh transmission lines, for example, we're still talking 5 to 6 years uh in many of the places that data centers are being built.

I think what is unique about the IIA is that we look uh obviously at the energy sector but we also look at the technology sector because trends in the

technology sector will determine also how big the impact of data centers are on the energy sector and here we've seen new bottlenecks emerge which are going to limit in the near term how many data

centers can be built. One of these, for example, is a manufacturing supply shortage for uh high bandwidth memory, which is needed in these really cutting

edge data centers that are used to train and run AI models, and where memory supplies are sold out for the next 2 to 3 years, where inventories have

collapsed uh down to 2 or 3 weeks, um and where prices have grown four-fold.

And so there's just not enough manufacturing capacity in the IT sector to s support you know more servers going into advanced AI data centers. And over

the last year or so there have been some profound disruptions to supply chains.

What can we say about the impact of that on the roll out of AI or is it too early to draw any conclusions? Well, we looked at the potential implications of what is

currently happening in the in the Middle East, which is disrupting energy supply chains. Um, and is also having knock-on

chains. Um, and is also having knock-on effects to some of the key components that go into the manufacturing of the IT equipment for for data centers. So, one

of these components, for example, is helium. So it's not as such an energy

helium. So it's not as such an energy commodity uh but it is a byproduct of natural gas production and is critical in the manufacturing of the advanced chips that are going into into data

centers. Uh and before the conflict in

centers. Uh and before the conflict in the Middle East about 1/3 of global supplies of helium transited the straits of humus. So this is something that we

of humus. So this is something that we we need a bit more time to see uh see how it will play out.

Okay. Now, how has the energy sector itself responded to some of these challenges, some of these bottlenecks?

What are some of the innovations we've seen from the sector? It's driving a a whole range of innovations in terms of how data centers are connected to the

grid and powered. Uh so first of all most data centers are connected to the grid and most under development want to connect to the grid because it's extremely reliable and provides relatively lowc cost uh electricity

relative to the alternatives which we can talk about. We estimate in the report that um in the next few years renewables power purchase agreements will cover about half of data center uh

electricity consumption and in the United States it's even more pronounced.

The tech sector is responsible for 65% of corporate procurement of renewables in the United States. So really helping to drive forward uh that sector in the

United States. But the innovations

United States. But the innovations extend also beyond uh renewables. So the

tech sector is driving the nuclear sector. So we saw since the last report

sector. So we saw since the last report power purchase agreements for roughly 7 gawatt of existing nuclear capacity be signed between utilities and uh the tech

sector. that's helping to extend the

sector. that's helping to extend the lifetime of uh these facilities and to fund refurbishments to make sure that they can run longer with the necessary

safety requirements. We're also seeing

safety requirements. We're also seeing the data center sector push forward small modular reactors which are a new kind of uh nuclear uh power plant design

and the pipeline is even bigger. Last

year we tracked about 25 gaw of what we call conditional offtake agreements. So

these are agreements that are conditional on the technology coming online because these are still an emerging technology. Uh in our latest

emerging technology. Uh in our latest report that 25 gawatt has grown to 45 gaw of conditional offtake agreements where essentially the tech sector is

saying if you build it we will take the power uh and that's providing a lot of pull for innovation in this in this technology.

Okay. Now AI offers huge opportunities for the energy sector. Now Sid, do you think that right now the energy sector is making the most of that potential?

So the energy sector of course is extremely capital intensive which means that there are you know cost pressures all around. It is a it is a sector where

all around. It is a it is a sector where there's a lot of data that's generated.

It's a sector that's extremely complex and you apply a technology to it that is able to sift through that kind of uh data generation that's able to sift

through that kind of complexity uh and offer solutions that can help make uh outcomes a little bit more efficient, a little bit more resilient. Those are the kinds of outcomes that we are already

seeing uh new startups and existing energy majors apply to their processes.

So to give you a couple of examples uh we have uh AI enabled weather uh forecasts that have improved the accuracy of forecasts and therefore uh

it helps reduce curtailment of renewable electricity into the grid. Uh another

example is the application of AI in helping ma make batteries uh run better so they're able to provide more energy output over a longer period of time. And

we find through our analysis that at the global level AI has the potential to reduce energy consumption by as much as the total energy demand by the country

of Indonesia by 2035. But of course, we're not really meeting that uh type of potential for a variety of reasons. uh

on the one hand uh we find that although there's a lot of data that's generated through energy processes through production consumption and supply that

data is not publicly available it's not open source uh and therefore it's hard to train models when that data is not available in the first place. In

addition to that uh we conducted a survey of energy companies and found that the single biggest challenge that these technology companies and these energy companies face is a lack of AI

skills and of course there are also regulatory challenges and policy challenges. This includes a lack of

challenges. This includes a lack of interoperability which means that if uh your say washing machine is not talking to the grid is not talking to the solar

generator in real time uh it won't know when to start operations and optimize on the generation of solar electricity as an example.

Okay. Now there's a consumer angle to this as well. Some people might be wondering if they live near a new data center what could that mean for their energy prices? So, what does the report

energy prices? So, what does the report tell us? Do more data centers

tell us? Do more data centers necessarily mean higher energy prices?

So, Dan, we conducted a lot of research on this and I wish I had a spicy answer for you, but I don't. Uh, our analysis revealed to us that it really depends on

uh the context and the conditions uh that we're tracking. So firstly we looked at all the different countries and states where data centers have been

uh growing and uh we could not find a clear relationship between electricity consumption growth and the rise of electricity prices in those regions. So

there was no clear relationship. But

having said that it is not to say that data centers don't contribute to rising prices. uh and to maybe you know explore

prices. uh and to maybe you know explore why that is we can uh take the analogy of uh the airline sector. So let's

assume two cities uh that is serviced by a specific airline and they have you know let's say multiple flights in a day. Uh if on this particular route we

day. Uh if on this particular route we find that the current capacity that's being utilized is say 50% that is half the seats in any plane are filled with

people. uh in this condition adding more

people. uh in this condition adding more passengers actually leads to the reduction in uh the the flight ticket prices right uh you need more capacity

utilization of something that is that expensive to purchase and that expensive to build in terms of the infrastructure for the airports and so on. However, if

this same route was uh had a occupancy rate of close to 100%. Then adding more and more demand and the inability to supply more flights to meet that demand

can lead to prices going up in the energy sector. Similarly, it's extremely

energy sector. Similarly, it's extremely capital intensive. So adding more demand

capital intensive. So adding more demand uh does not necessarily mean that electricity prices will rise as long as the existing capacity the the

infrastructure is being utilized better.

So costs are being spread around right but because data centers uh grow very quickly and the electricity system is unable to respond very quickly at the

same rates. it's not able to construct

same rates. it's not able to construct new uh grids and new electricity generation capacity at the same speeds.

Uh that mismatch can lead to pressures on prices. Uh but having said that uh so

on prices. Uh but having said that uh so far the relationship has not been clear-cut and uh you know in the report we offer a few directional measures that

countries and regulators can take to help mitigate any future pressures on prices.

Okay. Now, I think this is a good time to ask you both. When you look back at the report this time last year, what were the things that you either missed or the things that you got wrong? And

it's going to sound like I'm marking your homework, but maybe Thomas, you want to start us off?

I think this is an important exercise for for everyone to do when they're trying to analyze a topic uh that is so complex and that is moving so quickly.

So I would say you know broadly speaking our report last year was very comprehensive and it got most things right uh and it covered most of the issues that we've seen emerge over the

last 12 months. I would say uh there's one that we maybe missed um which we look at in depth in the in the new report and that's the rise of uh on-site

power generation options for data centers. Um so this is essentially a

centers. Um so this is essentially a powering option where the data center is not connected to the grid but rather has builds its own power plants on site uh

in order to supply its power needs largely uh through natural gas and essentially since our report in April 2025 we've seen a huge number of project proposals

uh still mainly at the proposal stage uh emerge in the United States. So we

wanted to look at this issue and to see whether this was uh a real viable trend uh for for data centers. What we find is that you know data centers are being bu

pushed to explore on-site power because grid connections are so slow. On-site

power raises complexity because uh data centers need very high reliability and that means that you need to overbuild your generation facilities to build in

the redundancy necessary to provide that very high reliability. It also raises costs uh quite substantially because you need to uh overbuild generation

facilities and it raises operational complexity as well because data center loads can actually vary quite a bit and that can put the equipment under stress.

Um and so you you see operators start to integrate things like battery storage plus natural gas turbines to match the reliability and and uh meet this

variability of of demand. So what we we we find in the report is that this is an extremely rapid emerging trend really over the last 12 months. Um there are

projects that are moving into the construction phase. So this is this is a

construction phase. So this is this is a real thing. But at the same time you

real thing. But at the same time you know with the constraints in gas turbine order supply chains with these additional complexities of uh the um uh

on-site business model it's not a silver bullet. We expect some data centers to

bullet. We expect some data centers to move forward with on-site power. Uh we

expect most data centers to still want to connect to the grid and even data centers that do move forward with on-site power maybe to look to connect to the grid at a later stage when it

becomes uh available. And if I could add uh to that uh another one we thought is related to robotics and what we call as broadly physical AI. So physical AI is anything that deals with the

intersection of artificial intelligence and the embodiment of uh of that AI. So

in other words, anything that can physically interact with the world and this includes robotics but also includes things like drones, self-driving cars and so on. So we do have some new

analysis on that. Uh and we find that firstly there has been a a consistent and growing uptake of industrial robots for example. Uh some of this also has

for example. Uh some of this also has implications on uh on industrial productivity. We find that uh the heart

productivity. We find that uh the heart of industrial competitiveness in the future may rely on how quickly they're able to uh use and deploy uh artificial

intelligence at various layers including robotics uh to help to reduce costs uh as well as increase production.

I want to end by looking ahead. So let's

just think about what that relationship between AI and energy could look like by 2030. Maybe you could both talk us

2030. Maybe you could both talk us through what you think the big themes could be. So in 2030 we expect that uh

could be. So in 2030 we expect that uh the electricity consumption of data centers will have more than doubled from where it stands today. But it will still

make up only about 3% of uh global electricity consumption. it will be much

electricity consumption. it will be much more in some regions uh and some specific markets. It might surprise the

specific markets. It might surprise the listeners to hear only a doubling. We

hear so much about AI today. Why is it only a doubling? And here I come back to the to the bottlenecks. So we think that you know our our projection for a doubling of the electricity consumption

uh of data centers is really at the upper end of what today's supply chains uh can can deliver. whether we talk about the IT equipment or whether we talk about the energy equipment and our

grids and and and so on. Um but this does mean that in 2030 we may be standing in front of you know even faster growth of the electricity

consumption of AI and data centers if things like agentic AI really take off you know if we have our AI agents running in the background doing our

shopping doing our you know day-to-day digital tasks and so on and that's really a question of how fast AI as a

technology moves itself right on On the other hand, we may find that AI has become so much more efficient that we're starting to run more and more on our

laptops and phones and really calling on data centers only for the really big models for the very sophisticated tasks and so on. And that means may mean that

we may start to see a plateau in in data center electricity uh consumption or we may see that you know the tremendous investments that Sid uh

mentioned uh at the beginning have run a bit ahead of monetization and so there's a bit of a pullback in data center uh construction because they're so capital intensive you know monetization was not

able to keep up or we might see that monetization has kept up and so you know the outlook is for continued growth of electricity consumption from data centers. So all of this to say, you

centers. So all of this to say, you know, we're faced with a really unique position in the energy sector today that the near-term is fairly well constrained. We know roughly where

constrained. We know roughly where things will be by 2030.

The investments have been made, the projects have been announced and many are already under construction and there's just not the capacity to go much faster. But by 2030, we could be

faster. But by 2030, we could be standing in front of a wide range of uncertainty.

A question for Sid too. What might the future look like?

The energy demand from uh AI or from data centers specifically has pretty much been baked in. This is

infrastructure that is being constructed as we speak. Uh this is uh electricity generation uh that is also being constructed to supply to this growing electricity demand. Uh on the other

electricity demand. Uh on the other hand, what's not baked in is the applications of AI in the energy sector.

So you know what we think of as more productive uses of AI across the economy to make it efficient to make it resilient to to optimize outcomes to make the energy system more innovative.

We we find that for example uh digital meters are actually a small subset of the total electricity meters that exist today. Similarly connected appliances

today. Similarly connected appliances are only a very small fraction of the total available appliances. So while

energy demand from uh data centers and AI has been baked in, what's not baked in is this potential to offset that increase. We find in our analysis that

increase. We find in our analysis that in fact the energy reductions from AI can more than offset the total energy demand uh growth from now until 2035.

uh but we really need to work towards overcoming those barriers and ensuring that we are able to make the best possible use of AI in the energy sector.

Okay, Sid Singh and Thomas Spencer, thanks so much for coming to talk to us today.

Thank you very much.

Thank you, Dan.