Speaker 1: This is Tom Rowland's Reese and you're listening to Switched
Speaker 1: on the BNF podcast. Today we're discussing BNF's new Energy Outlook,
Speaker 1: our annual flagship report that represents our long term energy
Speaker 1: and climate scenarios for the transition to a low carbon economy.
Speaker 1: AI data centers have dominated conversations in the clean energy
Speaker 1: sector of late, with forecasts varying wildly over just how
Speaker 1: much extra load there set to place on already strained
Speaker 1: power grids. As with every hot button topic, comes the
Speaker 1: risk of hype versus reality. While data center energy demand
Speaker 1: is unquestionably growing at speed, it currently represents just one
Speaker 1: point four percent of global power demand and only around
Speaker 1: two percent of that figure is actually consumed by AI facilities.
Speaker 1: The demand itself is also uneven, with some regions like
Speaker 1: the US needing significantly large amounts of power than others.
Speaker 1: And when it comes to meeting the needs of these
Speaker 1: energy intensive facilities, can the development of clean power sources
Speaker 1: even be done at the scale and pace required. To
Speaker 1: learn more about the forecast for data center energy demand,
Speaker 1: I'm joined by b and EF's head of Energy Systems Modeling,
Speaker 1: Ian Berrman to discuss findings from the twenty twenty five
Speaker 1: edition of our new Energy Outlook. BNF plans can find
Speaker 1: the full report at BNF go on the Bloomberg terminal
Speaker 1: or on BNF dot com, and if you're not yet
Speaker 1: a client, you can also download the executive summary at
Speaker 1: BNEF dot com. All right, let's get to talking about
Speaker 1: the impact of data centers on this year's NEO with Ian.
Speaker 2: Ian. Welcome to the podcast.
Speaker 3: Thanks Tom. So Ian has.
Speaker 2: Been on this podcast before.
Speaker 1: We had a freewheeling discussion around the power system as
Speaker 1: a whole. He had this crazy metaphor involving multiple people
Speaker 1: on like a bike, I mean, as in millions of
Speaker 1: people all on the same bike like this is not
Speaker 1: a very relatable metaphor. But okay, I got more messages
Speaker 1: on LinkedIn saying what a great episode after that particular
Speaker 1: one than I've ever had before, So I'm sure this's
Speaker 1: gonna be a fascinating discussion.
Speaker 2: I'm going to talk about NEO.
Speaker 1: So Ian tell us about NEO, which is obviously something
Speaker 1: as well that we hype a lot at BNEF, but
Speaker 1: so spell out what any means for those of us
Speaker 1: that are not familiar with it.
Speaker 4: NEO is the New Energy Outlook and it's benef's flagship publication.
Speaker 4: It's our house view on the future of the energy transition.
Speaker 4: We've been publishing it for many, many years now, since
Speaker 4: before I started, so seven or eight years ago, and
Speaker 4: in some ways it's the culmination of everything we do
Speaker 4: because we are looking at the entire energy transition. We're
Speaker 4: bringing in work from all our colleagues and all the
Speaker 4: various different sectors that touch on that into one comprehensive
Speaker 4: and coordinated house view on the.
Speaker 3: Future of energy and the transition.
Speaker 1: Awesome, and so I kind of have this view of
Speaker 1: it as this it's like an orchestra and we're pulling
Speaker 1: together everything that BNFS done in this coordinated way. And
Speaker 1: so where do you sit in this? AREI the composer,
Speaker 1: the conductor? Are you the star violinist?
Speaker 2: How do you read?
Speaker 1: Why is it that I'm talking to you on this
Speaker 1: podcast and not someone else? That's a good question. I'm
Speaker 1: I'm trying to figure out where to go with this analogy. Now,
Speaker 1: I'm definitely not the conductor that would be my boss.
Speaker 1: Do I make the instruments? Possibly?
Speaker 3: Possibly I write the music.
Speaker 4: Maybe that's that's the way so I sometimes will play
Speaker 4: along in an instrument when when and where I'm needed.
Speaker 4: But the Energy Systems modeling team, which I head up,
Speaker 4: our role is to work on the models which go
Speaker 4: into producing this. So I guess, I guess we make
Speaker 4: the instruments that other people play well.
Speaker 1: I feel like I mean, Firstly, obviously the people who
Speaker 1: make the instruments are fundamental to the orchestra, even if
Speaker 1: they don't get the limelight. So maybe I'm glad we're
Speaker 1: having you on this podcast so you can get your flowers. Finally,
Speaker 1: I feel like you might be being a little modest
Speaker 1: because the music that we create with NEO, I think
Speaker 1: very few people understand it in its completeness more than
Speaker 1: you do, which is which is why we have you
Speaker 1: on today. So Neo, it's this outlook to twenty fifty,
Speaker 1: a comprehensive look at everything.
Speaker 2: And obviously, if we're going to.
Speaker 1: Do that every year and just update our view on
Speaker 1: a twenty five year twenty five year plus forecast, it's
Speaker 1: not you know, you question why we're doing it, because
Speaker 1: not that much can Chette have changed or how we
Speaker 1: see twenty fifty between one year and the next. So
Speaker 1: we always do new things to kind of keep it fresh,
Speaker 1: keep it interesting, have some other insight to say.
Speaker 2: So this year, what would you say the focus was?
Speaker 4: So this year, which is probably not going to surprise
Speaker 4: anyone that's listening, the hot topic has been data centers
Speaker 4: and AI, so we went away. We developed our own
Speaker 4: in house short and long term forecasts for electricity demand
Speaker 4: from data centers and AI. That's a big feature in
Speaker 4: the report. We refreshed our entire base case, which we
Speaker 4: call the economic Transition scenario, which is not exactly a
Speaker 4: business as usual scenario. And it's probably worth unpacking that
Speaker 4: very briefly because I think economic Transition scenario really reflects
Speaker 4: what makes I think NEO unique and BNF's approach to
Speaker 4: producing this type of global analysis unique, And what makes
Speaker 4: the Economic Transition scenario unique is that we're taking an
Speaker 4: economics led approach, and we're doing this informed where we
Speaker 4: can by bottom up modeling, so very detailed and granular modeling.
Speaker 4: And when we say we're putting economics in front, that
Speaker 4: means we're trying to strip out policy, particularly if that
Speaker 4: that's aspirational, any sort of targets which are non binding,
Speaker 4: et cetera. And we're really trying to hone in on
Speaker 4: the technology and economics story, and that this leaves us
Speaker 4: with a story where economics are in the driving seat
Speaker 4: and technology is a story.
Speaker 1: Right, it's almost I mean, this is a fair way
Speaker 1: to describe it. What we're painting there is a picture
Speaker 1: of a world that follows what we would consider to
Speaker 1: be an economically rational ideal without the interference of inconvenient
Speaker 1: human beings who are either pushing for more low carbon
Speaker 1: technologies to reflect the challenges or the crisis should say
Speaker 1: of climate change or other ideologies, or one could even
Speaker 1: say global dysfunctions that can get in a way of
Speaker 1: maybe that pure economic vision.
Speaker 3: Yeah, exactly.
Speaker 4: And policy can push to carbonization faster or slower than
Speaker 4: what the economics would tell you is least cost. And
Speaker 4: as soon as you try and produce like a four
Speaker 4: car if you will, and you're making judgment calls on
Speaker 4: what policies are announced, when, how long they last for, etc.
Speaker 4: You're really putting your analysis on shaky ground. So by
Speaker 4: focusing on that economic store, it's always useful to know
Speaker 4: what the least cost system is, even if you don't
Speaker 4: know what politics are going to be. And I don't
Speaker 4: think many of us know what politics are going to
Speaker 4: be at the moment.
Speaker 1: We do forecast some sort of human I say, brilliant
Speaker 1: if I might, in terms of we look, we do
Speaker 1: take into account cost declines of technology and the sort
Speaker 1: of technological progress we expect to have happen. But we yeah,
Speaker 1: we don't forecast the politics. We don't forecast either people
Speaker 1: being able to miraculously get on the same page. And
Speaker 1: we don't forecast in which particular way people are dividing
Speaker 1: among themselves and tearing each other apart, which is obviously
Speaker 1: impossible to forecast. So it's kind of like a north
Speaker 1: star in a certain sense.
Speaker 4: North starts a good way to put it, and may
Speaker 4: I also don't want to give people the impression that
Speaker 4: it's sort of too cold and rational and is not
Speaker 4: based on the real world necessarily. And I think that
Speaker 4: there's two main reasons why that's not the cases. First,
Speaker 4: I mean, from our sector teams, we do have a
Speaker 4: very good handle on what we think will happen in
Speaker 4: the next at least at least five years for most sectors.
Speaker 4: We've got huge project databases that BNF is very very
Speaker 4: famous for renewable assets and other types of asset classes,
Speaker 4: so we know what's coming and that feeds into the model,
Speaker 4: and it's sort of there's a transition away from what
Speaker 4: is known and fixed in the short term for the
Speaker 4: next few years, and then what the model proposes to
Speaker 4: do after that, and that's sort of a gradual transition
Speaker 4: and handover in the results.
Speaker 3: And then also, not.
Speaker 4: All sectors that we model are as rational, as for example,
Speaker 4: the power system, and so I mean consumer behaviors in
Speaker 4: important part of how we model our ev uptakes. So
Speaker 4: that's just one example of where it's not like completely
Speaker 4: without taking humans into consideration. But the focus is the economics. Ultimately,
Speaker 4: that's the story we're trying to tell.
Speaker 1: And my impression that that's complex enough as it is.
Speaker 1: I think that you said something interesting before the podcast.
Speaker 1: That is the number one kind of piece of feedback
Speaker 1: you get from clients about this is that they don't
Speaker 1: like a particular kind of charts. Tell me what kind
Speaker 1: of chart don't they like?
Speaker 4: Yeah, So I've been involved in here for a while now,
Speaker 4: and I mean, sometimes, well maybe this is feels like
Speaker 4: a couple of years ago now, but sometimes there's not
Speaker 4: big disturbances in markets, and things tend to tick along,
Speaker 4: and I feel like people sort of get used to
Speaker 4: charts and they can go up, they can go down,
Speaker 4: but they tend to be sort of like quite constant.
Speaker 1: They show a direction, they show a direction, which direction
Speaker 1: plays itself out in a way that you're like, okay, cool,
Speaker 1: so that's the trend.
Speaker 3: And yeah.
Speaker 4: I mean one of the charts that I've got the
Speaker 4: most questions about, or the general type of charts, is
Speaker 4: where that doesn't happen, where you'll have something that goes
Speaker 4: up and then it goes down and people immediately latch
Speaker 4: onto that and they're like, what's happening here? And I mean,
Speaker 4: I think that's part of what makes us a unique
Speaker 4: cause we try to do that bottom up modeling, and
Speaker 4: I think when you do that, you can see these
Speaker 4: sort of weird changes of directions occasionally in your results.
Speaker 4: I think if you're doing a trend based analysis more
Speaker 4: top down, you wouldn't see that at all.
Speaker 3: You would miss that.
Speaker 4: I'm always amazed by client's ability to hone in and
Speaker 4: identify those charts that change tackicy. It has come at
Speaker 4: me with questions.
Speaker 1: If you're been a f client, presumably by the fact
Speaker 1: that you even find our content useful. You do not
Speaker 1: fear change, but you might still fear change of change.
Speaker 1: And that's what those charts are, is like the change.
Speaker 1: We've got some change, and then the change changes, and
Speaker 1: that's a little bit weird.
Speaker 4: Yeah, I mean, it's probably usual to talk about a
Speaker 4: little bit. I mean, the main culport of this has
Speaker 4: been when you zoom out, our results are basically saying,
Speaker 4: if you look at US gas demand, we see a
Speaker 4: decline until twenty thirty and then growth again, and sort
Speaker 4: of people immediately what's behind that, And it's not a
Speaker 4: super complex idea to unpack. Essentially, what we have today
Speaker 4: are many parts of the US where we're still building renewables,
Speaker 4: and renewables are still in the money, and that expansion
Speaker 4: of renewables continues, and so there's some displacement of natural
Speaker 4: gas demand in the power system towards twenty thirty. But
Speaker 4: then around twenty thirty at least when we've modeled this
Speaker 4: to the last year or two, I mean, the whole
Speaker 4: system is moving so equally. Reinpoints the wrong sort of
Speaker 4: terminology here, but we sort of reach a penetration level
Speaker 4: of renewables where the system struggles to move past that
Speaker 4: and so post twenty thirty, what happens. We still build renewables,
Speaker 4: but what you see is the whole system is getting bigger,
Speaker 4: and renewables role in that system grows more or less
Speaker 4: in proportion with that system wide growth, which means the
Speaker 4: other parts of the system also grow in proportion there
Speaker 4: and you see a return to gas demand growth after
Speaker 4: twenty thirty.
Speaker 1: I would go so far as to say that this
Speaker 1: is part of the real value add I mean the
Speaker 1: fact that you have these and clients hone in on
Speaker 1: them and ask questions. And maybe it's not because they
Speaker 1: fear the change that's happened to change. It might just
Speaker 1: be that they've actually identified this is where the value
Speaker 1: really is. Is Some of these things kind of reminds
Speaker 1: me back in my early days as an analyst. My team,
Speaker 1: as an April Full we wrote a pretend research note
Speaker 1: and sent it to the editors.
Speaker 2: I mean, this is how nerdy we were.
Speaker 1: And in it we had a forecast, we had the methodology,
Speaker 1: and we had developed a methodology called the IACAGR, which
Speaker 1: stands for Indiscriminate application of a compound annual growth rate.
Speaker 1: So obviously just making fun of like how you can
Speaker 1: make something look with the sort of like the laziest
Speaker 1: possible methodology, and I think this just really emphasizes that
Speaker 1: you definitely haven't done the indiscriminate application of a compound
Speaker 1: annual growth rate. There's a lot of complexity behind what
Speaker 1: you've done, and so even with some of the policy
Speaker 1: interventions going one way or other stripped out, there's still
Speaker 1: a lot of the zigzagging that happens within this as
Speaker 1: some of these different dynamics play out. So speaking of
Speaker 1: different dynamics that up playing out, let's get on to
Speaker 1: one of the big additions of this report, which was
Speaker 1: bringing in an analysis of the impact that data centers,
Speaker 1: principally driven by power demand for AI, how they could
Speaker 1: impact the energy transition. So for full context, we did
Speaker 1: a podcast with our colleagues Natalie Lamandebrata and Helen co
Speaker 1: and they were talking about specifically maybe the nearer term
Speaker 1: view and particularly to the US, although I think a
Speaker 1: lot of those themes apply more broadly, and so that
Speaker 1: was part of obviously your analysis, but you also looked
Speaker 1: in the longer term as well. You extended this out
Speaker 1: to twenty fifty and obviously the team as well took
Speaker 1: this global. So, yeah, what were some of the things
Speaker 1: that you found doing this analysis?
Speaker 4: So, I mean, first off, I'm definitely standing on the
Speaker 4: shoulders of giants when it comes to the work that
Speaker 4: Natalie and Helen have done here. This is a couple
Speaker 4: of couple of interesting dynamics here is one, because of
Speaker 4: latency concerns, that forward looking view of where where is
Speaker 4: data center demand coming from? It's a lot more globally
Speaker 4: distributed than you might otherwise imagine. I mean that's not
Speaker 4: to downplay the role that the US plays, Like the
Speaker 4: US is still huge, say China's massive.
Speaker 1: Approaching fifty percent of data centers today are in the US.
Speaker 4: I think, well, yeah, no, that's that's more or less
Speaker 4: on the money. And yeah, and so we see like
Speaker 4: a large geographic spread, at least towards the longer term.
Speaker 4: And there's sort of a dynamic here where I mean,
Speaker 4: the US is definitely sort of leading the pack here,
Speaker 4: but other economies who might be a bit later that
Speaker 4: party will eventually catch up in their demand for general
Speaker 4: data and AI specific use cases will grow.
Speaker 3: Over time too.
Speaker 4: There's also I mean there's a seg here as well,
Speaker 4: because that story of renewed gas demand growth in the
Speaker 4: US post twenty thirty is partly responsible from data centers
Speaker 4: as well. There's definitely a long term growth there data
Speaker 4: centers and interestingly electric vehicle demand as well. So that
Speaker 4: was another sort of high level result that popped out
Speaker 4: which was quite interesting, is that evs in most places
Speaker 4: by twenty thirty is still much more significant demand than
Speaker 4: data centers may potentially not much more significant. Other places
Speaker 4: are a bit closer, and I mean US is one
Speaker 4: of the few exceptions where we think that data center
Speaker 4: demand will outpace demand from EV's in the short term,
Speaker 4: but eventually I think EV's will catch up by about
Speaker 4: twenty thirty.
Speaker 2: So where else would we see that trend.
Speaker 3: That's a good question.
Speaker 4: I think we're probably if you looked at markets where
Speaker 4: EV growth is a bit behind the curve, where you
Speaker 4: still have strong demand from data centers. So I think
Speaker 4: I haven't seen these charts, but my guesses would be
Speaker 4: potentially places like Australia or Malaysia where there's a reasonable
Speaker 4: amount of data centers going in but they're not necessarily
Speaker 4: particularly strong EVA markets.
Speaker 2: It's really interesting.
Speaker 1: I mean, actually we just recorded a podcast with Colin Mcherrika,
Speaker 1: who heads up our advanced Transport analysis, and we were
Speaker 1: talking about EVO, which is the transport counterpart to NEO,
Speaker 1: So that's the electric vehicle out look, and we had
Speaker 1: a long discussion about how the US is going to
Speaker 1: be moving slower than other markets on electric vehicles. But
Speaker 1: you know, it's interesting there's this pattern that some of
Speaker 1: those markets they might be getting some data centers, so
Speaker 1: there's going to be demand growth either way. I remember
Speaker 1: a really interesting point that you made in a meeting
Speaker 1: when we were just talking about our data center analysis,
Speaker 1: because you know, there's this question that is sort of
Speaker 1: everyone wants the answer to all this data center demand,
Speaker 1: all this electric vehicle to Is it going to mean
Speaker 1: more renewables and more gas? Is it going to mean
Speaker 1: more emissions? And I remember you explaining this really nuanced point,
Speaker 1: which is that you can create a model that has
Speaker 1: all of those extra things layered in and you can
Speaker 1: see the difference to the power generation mix. But it's
Speaker 1: not necessarily correct to just say that the difference to
Speaker 1: the mix is what is attributing that to those data
Speaker 1: centers or electric vehicles? Can you just explain that? Am
Speaker 1: I remembering this right?
Speaker 2: You are?
Speaker 4: And I mean there's a there's a chart in NEO
Speaker 4: which I think illustrates this point quite well. So we
Speaker 4: with our economic transition scenario, we modeled in an additional
Speaker 4: scenario sort of behind the scenes, where we stripped out
Speaker 4: demand from data centers, and so we solved these two
Speaker 4: nearly identical scenarios. The only difference is that missing data
Speaker 4: center demand. And you can then look at those two
Speaker 4: sets of results and compare them and start to infer
Speaker 4: what the effects are of that data center demand. And
Speaker 4: this goes into a chart which we have in the
Speaker 4: report where you see that potentially out to twenty thirty,
Speaker 4: about two thirds of the additional demand and for data
Speaker 4: centers is met by fossil fuels. And you can't stop
Speaker 4: there though, because that's not the actual message. That's what
Speaker 4: the chart shows. But you sort of have to unpack
Speaker 4: what that sensitivity means, and I think it goes back
Speaker 4: to this concept of additionality. So, first off, we're not
Speaker 4: saying that two thirds of the electrons that are going
Speaker 4: to go into data centers are going to come from
Speaker 4: fossil fueled plants at all.
Speaker 3: That's not what we're saying.
Speaker 4: I think we're probably saying the opposite I think we're
Speaker 4: looking at the sort of corporate ppa activity and there's
Speaker 4: like huge demand there. So like these data centers at
Speaker 4: least normally are going to be powered more often than
Speaker 4: not by renewables, and that's not just a US trend.
Speaker 1: And those I mean, the interesting thing about it as
Speaker 1: a market, and this is very different from electric vehicles,
Speaker 1: is it's so concentrated with a small number of very
Speaker 1: huge organizations that have considerable power in the market. Yeah,
Speaker 1: and a lot of those organizations are investing heavily in
Speaker 1: renewables supply, so.
Speaker 4: And nuclear and other things as well. And I mean
Speaker 4: we also hear stories of them building on site gas
Speaker 4: generation as well. So there's a lot of things happening.
Speaker 4: But I think this concept of coming back to this
Speaker 4: concept of additionality. So we're not saying that two thirds
Speaker 4: of the power that goes into these things is coming
Speaker 4: from fossil fuels. What our analysis shows is that when
Speaker 4: you add this additional demand to the system, can we
Speaker 4: meaningfully increase the rate at which we build renewables in
Speaker 4: the next five to ten years And our analysis tends
Speaker 4: to indicate that we can't. And so this is the
Speaker 4: important idea of additionality it we're going to build these
Speaker 4: data centers and nominally they're going to be powered by renewables,
Speaker 4: But that doesn't mean extra renewables built in the world.
Speaker 4: It probably just means renewables that were already going to
Speaker 4: be built somewhere else now being built and attributed to
Speaker 4: data centers. Does that means the rest of the system
Speaker 4: becomes a little bit more more fossil heavy. And I
Speaker 4: think that's the way to understand the chart. That concept
Speaker 4: of additionality is that we're not building many more renewables
Speaker 4: because of this additional demand, and the reason being we're
Speaker 4: sort of near or at the limits of what we
Speaker 4: think renewable supply chains can manage in the next five
Speaker 4: to ten years and what the grid could manage in
Speaker 4: the next five to ten years.
Speaker 1: I mean, it's really interesting because I think where we
Speaker 1: get to here is on that boundary between our economically
Speaker 1: rational model and something that reflects the what happens in
Speaker 1: the fog of war. And I could make the argument
Speaker 1: that when you say all those renewables would have been
Speaker 1: built anyway, but now they're going to be built with
Speaker 1: a PPA for a data center, you could make the
Speaker 1: argument actually they weren't going to get built anyway, even
Speaker 1: though it was the economically rational thing from a system
Speaker 1: point of view, because might just be the market design
Speaker 1: didn't favor that. So I and we also have to
Speaker 1: keep that in mind, is that sometimes these things can
Speaker 1: act as of forcing mechanism to make maybe the outcome
Speaker 1: that should have happened anyway on an economic basis happen.
Speaker 1: I think you're entirely right, and that's I was careful
Speaker 1: my language. I didn't I used infer because the results
Speaker 1: don't tell you something's definitely going to happen. It just
Speaker 1: gives you a hint at what might be what might
Speaker 1: be the reasons. And I mean I've also made a
Speaker 1: similar argument before I said, Look, before we started talking
Speaker 1: about data centers an AI, we already were running to
Speaker 1: huge challenges in trying to connect the renewables that we
Speaker 1: were already going to build over the next five to
Speaker 1: ten years. We were running into connection Q issues and
Speaker 1: some supply chain issues. And this is before we started
Speaker 1: worrying about tariffs and whatnot. So just connecting the amount
Speaker 1: of renewables that we thought was economically irrational in our
Speaker 1: modeling over the next few years was already a challenge,
Speaker 1: and we were already constraining the model there. And to
Speaker 1: flip what I just said on his head, like, the
Speaker 1: amount of resources that the Amazons, the Googles, the Microsoft,
Speaker 1: and the Metas have in the world are enormous, so
Speaker 1: they can actually apply the capital and political influence required
Speaker 1: to in many cases solve some of these issues that
Speaker 1: might have persisted for longer otherwise. So we've gone from
Speaker 1: a situation where we didn't necessarily know how we were
Speaker 1: going to sort of muster the support we needed in
Speaker 1: these various areas to build this amount of renewables, and
Speaker 1: all of a sudden, we've got these huge players like
Speaker 1: pushing all the right buttons to try and get these
Speaker 1: things built as quickly as possible. If we sat here
Speaker 1: in a year's time, I'm assuming that in the NEO
Speaker 1: twenty twenty six we will also have inc data centers,
Speaker 1: because we can't not include them unless in the next
Speaker 1: year or so everyone decides that actually, AI was such
Speaker 1: a terrible idea.
Speaker 2: And you know, I highly doubt that's going to happen.
Speaker 1: And one of the things as you were speaking that
Speaker 1: I was thinking about was this idea of on site
Speaker 1: generation and that's been discussed a lot as a potential solution.
Speaker 1: I suppose I've got this question all in the wrong order.
Speaker 1: I should have said, did we model on site generation
Speaker 1: this year? And do you think we will model it
Speaker 1: next year?
Speaker 3: So it's a good question.
Speaker 4: So the short answer is no, So all our demand
Speaker 4: is grid connected. That doesn't mean to say there aren't
Speaker 4: constraints that we can apply to the grid in the model,
Speaker 4: but all demand is grid connected. I don't think it's
Speaker 4: a terrible assumption for data centers and AI. So I mean,
Speaker 4: we've talked before about this five nines reliability constraints. So
Speaker 4: we need ninety nine point nine nine nine percent uptime
Speaker 4: on many of these facilities, which are highly critical parts
Speaker 4: of the infrastructure that powers the modern world. And I mean,
Speaker 4: when you do the math, ninety nine point nine o
Speaker 4: nine of all the hours in the year of cross
Speaker 4: the year gives you five minutes of downtime. And so
Speaker 4: it's very hard to design a system that's off grid
Speaker 4: that only gives you five minutes of downtime with a
Speaker 4: high chance of likelihood.
Speaker 1: I mean, I suppose there's being completely off the grid,
Speaker 1: which might create those issues. But oh sorry, we're looking
Speaker 1: at this from the site. Yeah, two separate things here.
Speaker 4: So when it comes to on site generation, so that
Speaker 4: in theory is something that our model proposers, so we
Speaker 4: don't make a judgment call of where that generation has
Speaker 4: to sit in the grid. So some of the generation
Speaker 4: that our model proposers could be on site generation for
Speaker 4: those facilities.
Speaker 1: Okay, So actually, in a way you do factor it
Speaker 1: in kind of implicitly, but we don't make it explicit exactly.
Speaker 4: So everything's grid connected, but we don't make a judgment
Speaker 4: call on where things have to sit in the grid
Speaker 4: for the whole thing to make sense.
Speaker 1: And whether it's one four hundred megawatt gas plant or
Speaker 1: four hundred one megawat gas plants exactly.
Speaker 4: I mean, I think for the reliability reasons we mentioned,
Speaker 4: it might be more likely to build more smaller gas
Speaker 4: plants for one of these things rather than one line
Speaker 4: one which is harder to maintain it that uptime level.
Speaker 1: So it sounds like in a way you have got
Speaker 1: that covered. And obviously we're making no promises here, but
Speaker 1: what do you think might be some of the things
Speaker 1: that we add in next years NEO.
Speaker 3: Then when it comes to data centers.
Speaker 4: I mean, I think we just we'll have a year's
Speaker 4: more data behind us, and we'll have a better view
Speaker 4: on what the short and then the long term looks like.
Speaker 4: And one of my favorite data points this year is
Speaker 4: if you look at the data center demand that we
Speaker 4: put in the model, the fraction of that which is
Speaker 4: AI facilities at the moment, it's about two percent of
Speaker 4: old data center capacity is AI specific and the rest
Speaker 4: is just our general run of them, will data centers,
Speaker 4: backbone of the Internet, the cloud, et cetera. So that's
Speaker 4: just two percent of the total. And then data centers
Speaker 4: as an entire demand class are only about one point
Speaker 4: four percent of the entire global power demand.
Speaker 1: And so so right now this is like a small
Speaker 1: fraction of a small fraction.
Speaker 3: A small fractions for it.
Speaker 4: And I love an analogy, Tom, so indulge me, Andre
Speaker 4: we go.
Speaker 2: We haven't had enough to date.
Speaker 4: So the way I like to think about it, it's,
Speaker 4: right right now, this demand from AIS just a very
Speaker 4: very small ripple on the horizon we're looking out to
Speaker 4: see and we can see this very very small ripple,
Speaker 4: and we really don't know in five or ten years
Speaker 4: time whether this is just going to be a small
Speaker 4: wave that sort of peters out or a tsunami.
Speaker 3: That washes the power system away.
Speaker 4: We really don't know at the moment, but there are
Speaker 4: some clues because, to continue this analogy, the coastline's not flat.
Speaker 4: There are parts of this coastline which extend file into
Speaker 4: the sea, and we can look at these places today
Speaker 4: and see the challenges they're facing. So, I mean, it's
Speaker 4: not a surprise we're going to talk about PGM now.
Speaker 4: Basically the part of the US, one of the grids
Speaker 4: in the US where we see a hyper concentration of
Speaker 4: data centers.
Speaker 1: Yeah, that's like forty percent of US state centers roughly,
Speaker 1: I think are in PJM.
Speaker 4: Yeah, it's it's it's absolutely massive, And so you can
Speaker 4: look to those regions now and you can see the
Speaker 4: challenges they're facing and the struggles they're going through, and
Speaker 4: you can sort of think about the implications for the
Speaker 4: wider system further down the path.
Speaker 1: So I'm I'm running with your rather doomsday like analogy. Here,
Speaker 1: You're on the beach, there's a little ripple. Some people
Speaker 1: get up and start running, and other people are like, really,
Speaker 1: you just need to chill.
Speaker 2: There's nothing going on here? Where are you?
Speaker 1: Are you grabbing your towel and making it for the mountains,
Speaker 1: or are you just a bit calmer waiting to see
Speaker 1: what will happen, and whatever happens, you're like, I'm pretty
Speaker 1: confident we can handle this.
Speaker 4: So if you look at our forecast compared to other
Speaker 4: research houses, we're more likely to be the ones staying
Speaker 4: on the beach ordering another dakery. So we tend to be.
Speaker 1: Or towards while the dakery is are cheap because no
Speaker 1: one else is around to buy them as well.
Speaker 4: Exactly, So we tend to be the more towards the
Speaker 4: more conservative end of the demand forecast that you see
Speaker 4: for data centers and AI out there.
Speaker 1: But put that in numbers because like our twenty fifty
Speaker 1: I think we still say data sense will be a
Speaker 1: pretty significant percentage.
Speaker 2: Of demand in twenty fifty of power demand.
Speaker 4: Yeah, we're we're going from about one point four percent
Speaker 4: of global power demand today by twenty thirty that's about
Speaker 4: three percent, and by twenty thirty five that's four point
Speaker 4: five percent. If we keep going out to twenty fifty,
Speaker 4: it's almost nine percent of all electricity demand.
Speaker 1: And to be clear, I mean we're seeing we're assuming
Speaker 1: a certain amount of electrification generally in kind of all economies,
Speaker 1: So a bigger percentage of a bigger volume, right, Yeah.
Speaker 4: So the system is getting huge and yeah, and I
Speaker 4: think another thing is that, like originally I said, like
Speaker 4: some of this demand, there's more, maybe more of a
Speaker 4: spread across geographies than you might think. It's not just
Speaker 4: the US only story, but I mean the US definitely
Speaker 4: is at the pointy end of the wedge here, and
Speaker 4: particularly that that PGM region we mentioned. So, I mean
Speaker 4: those figures might be in twenty fifty, like eight point
Speaker 4: seven percent worldwide, but it's it's closer to about twenty
Speaker 4: five percent in PGM at that point.
Speaker 2: Wow.
Speaker 1: Okay, it's really interesting. So it might be the own
Speaker 1: s beaches have another Dakari and another one. Pick up
Speaker 1: your towel and start getting ready for change. I think
Speaker 1: this wave is not going to hit all beaches equally.
Speaker 3: Yeah, in case it.
Speaker 1: Lost a thread of this metaphor, I mean that some
Speaker 1: regions and some markets will be affected by this more
Speaker 1: than others.
Speaker 3: Just to be clear, Yeah, no, I think that's a
Speaker 3: good way to put it.
Speaker 4: I think it's not panic stations everywhere. It's also it's
Speaker 4: not also not an insignificant challenge, Like if we are
Speaker 4: going to keep building data centers, particularly at the pace
Speaker 4: that we currently want to build them, there are huge
Speaker 4: challenges in both in the short term and the long
Speaker 4: term to become a reality. And I think part of
Speaker 4: the challenge of the modeling if you sort of zoom
Speaker 4: out a little bit, and this is maybe more a
Speaker 4: story for sort of the more developed economies, but we've
Speaker 4: been doing neo for a while now, and if you
Speaker 4: look back at electricity demand over the last like ten
Speaker 4: or so years, I think we basically got out of
Speaker 4: the habit of having new demand. We were quite comfortable
Speaker 4: with demand being quite flat and they're not being demand growth,
Speaker 4: and we just sort of were quite happy to sit
Speaker 4: there and twiddle our thumbs. And if in many regions
Speaker 4: in particular because of basically energy efficiency gains, partly because
Speaker 4: of like the shift from industrial to service economies, you
Speaker 4: even saw falling demand. And so I mean there's a
Speaker 4: regional variation here, but like basically, if you ignore evs
Speaker 4: and data centers, a lot of places, the more developed
Speaker 4: parts of the world would see falling demand over the
Speaker 4: next ten or twenty years, and then you're add in
Speaker 4: evs and it sort of evens out to a bit
Speaker 4: more stable, and then when you ad data centers on
Speaker 4: top of that, you're getting growth pretty much everywhere.
Speaker 1: Now it's so interesting, And you know, I head up
Speaker 1: our global power market analysis where we focus a lot
Speaker 1: on the outlook for prices, and I think that and
Speaker 1: this was before I was heading this up, but before
Speaker 1: this wave of demand growth came into town. The themes
Speaker 1: that we were talking about was like price cannibalization. How
Speaker 1: much is price going to get cannibalized by these renewables,
Speaker 1: And that is still an important topic, but that is
Speaker 1: also predicated on this idea of kind of flat or
Speaker 1: declining demand. Now you've got demand growth, It's like, yeah,
Speaker 1: price cannibalization is still a factor, but it might be
Speaker 1: offset by other factors as well, including the growth of demand.
Speaker 1: So suddenly it's a lot more interesting and creates opportunities.
Speaker 1: I think this is the key point is a lot
Speaker 1: of new power capacity will be renewable, and it maybe
Speaker 1: provides a more optimistic outlook for the economics of that
Speaker 1: power capacity as well. So I would say that even
Speaker 1: if we're saying in our economic modeling that there might
Speaker 1: be more gas consumed, which from an environmental point of
Speaker 1: view is not good. Of course, people in the renewable
Speaker 1: energy industry should be looking at this as and I
Speaker 1: think they are looking at this as an opportunity for
Speaker 1: them to make their mark. And how it actually plays
Speaker 1: out in reality, whether it's majority of gas generation meeting
Speaker 1: that demand or renewables, it kind of depends on how
Speaker 1: people play their cards and strategize and make the right moves.
Speaker 1: And so it's going to be really interesting to see
Speaker 1: all of that. But the point is in the power sector,
Speaker 1: suddenly it's not just about keeping the lights on.
Speaker 2: There's opportunity as well.
Speaker 4: Yeah, and I mean it is, it's just more dynamic.
Speaker 4: Is more exciting when you when you have to accommodate
Speaker 4: new growth, and I mean first level, it makes my
Speaker 4: job more interesting. It's you've got to sort of that
Speaker 4: double whemmy challenge of like what are the new things
Speaker 4: we build and where do we canniblize and potentially replace
Speaker 4: or retire the old assets.
Speaker 1: Well, for every new dynamic, that is another kink in
Speaker 1: one of your charts. That is more the change is changing,
Speaker 1: and that is more questions from clients asking for a
Speaker 1: more detailed explanation of what is going on. So Ian,
Speaker 1: thank you very much for joining today.
Speaker 3: Nice Thanks Tom, See you on the beach.
Speaker 2: See on the beach. Today's episode of Switched On was
Speaker 2: produced by Cam Gray with production assistants from Kamala Shelling.
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