Speaker 1: With Laurent's Segeleen from London and Gerard read from Berlin.
Speaker 2: This is redefining energy today.
Speaker 3: On redefinding energy Jihad, it's a very hot topic. We're
Speaker 3: going to talk about AI data centers and the cost
Speaker 3: of speed. Yeah.
Speaker 1: I like the way I say that the cost of
Speaker 1: speeder on because it definitely does say that we're in
Speaker 1: an AI race and the winner seems to be the
Speaker 1: one that's the fastest. At least that's what the industry
Speaker 1: thinks anyway.
Speaker 2: The first award from our new partner. Today's show is
Speaker 2: supported by the BMW Foundation, Harvard quant The BMW Foundation
Speaker 2: unites leaders from diverse sectors to develop solutions that foster
Speaker 2: an innovative economy and a future proof society. A key
Speaker 2: focus is the energy transition and climate change, where the
Speaker 2: Foundation drives international collaboration to accelerate the energy transition. With
Speaker 2: rising energy demands from AI and data centers, new partnerships,
Speaker 2: effective collaboration, and the exchange of science based solutions and
Speaker 2: strategies are essential. That's why the BMW Foundation supports this
Speaker 2: podcast and bring these discussions to global stages by hosting
Speaker 2: the Energy Security Hub at the Munich Security conference in
Speaker 2: twenty twenty six, streaming live from February twelve to the fourteenth.
Speaker 2: You can learn more at the BMW Foundation dot org.
Speaker 3: Back to the show. Yeah, and of course the greed
Speaker 3: being on decade long planning, and all of a sudden
Speaker 3: you get three mega what's coming in one giga? What's
Speaker 3: coming there? And the grid simply cannot follow, which means
Speaker 3: that it's all in on deck and there's a lot
Speaker 3: of things happening behind the meta. And you know, we're
Speaker 3: going to talk in details about those OCGT open cycle
Speaker 3: gastar binds or maybe fuel cells and batteries. Basically they're
Speaker 3: throwing everything they can just to get some energy.
Speaker 1: It's a really offsy topic to talk about, so and
Speaker 1: it's great to actually bring an expert on that knows
Speaker 1: the space very well. And we ended up finding Andrew Perry,
Speaker 1: who's the director of the Energy Transition and Environmental Business
Speaker 1: Unit at Faculty AI, which is leading consultancy businesses in
Speaker 1: the AI space in Europe.
Speaker 3: Yeah, and the interesting thing is that it's coming from
Speaker 3: the AI world and it's not coming from the nuts
Speaker 3: and boards world. So he show us the moon, we
Speaker 3: look at the finger. We have a very good conversation.
Speaker 3: So let's bring on on de Perry on the show
Speaker 3: and the welcome to the show.
Speaker 4: Pleasure to be here, thank you for having me.
Speaker 1: Andy. Maybe let's jump straight in. I mean, what we're
Speaker 1: really going to do is talk about AI and energy,
Speaker 1: try to get a better understanding of that. But maybe
Speaker 1: just talk a little bit about what's going on in AI.
Speaker 1: I think that'd be a great starting point.
Speaker 5: The technology development is, if anything, progressing quicker than what
Speaker 5: people have been expecting.
Speaker 4: Recently.
Speaker 5: There's something called the International Mass Olympiad, which is like
Speaker 5: the premiere high school competition for math. And if you
Speaker 5: looked at the bedding markets, so like the kind of
Speaker 5: predicted markets, the prediction was abound twenty percent that AI
Speaker 5: could could win that competition in this year, so twenty
Speaker 5: twenty five, And actually it won that competition in July,
Speaker 5: and then a month later it then achieved a perfect
Speaker 5: score in the ICPC, which is the International Collegiate Programming Contest,
Speaker 5: which is basically where top university teams come together and
Speaker 5: solve like complex algorithmic problems. This was GPT five. I
Speaker 5: thinkam GEM and I both were able to achieve a
Speaker 5: perfect score of.
Speaker 4: Out twelve, which none of the human teams managed that.
Speaker 5: Pace of change listening incredibly quick and so it's beating
Speaker 5: benchmark every which way. I think the benchmarks effectively meaningless
Speaker 5: now because because all the benchmarks effectively and decent, And
Speaker 5: so when you look at the rate of progress and
Speaker 5: you look at it over time, it is possible actually
Speaker 5: to understand it in a way that doesn't always seem possible.
Speaker 5: So it sometimes feels like it's just this exponential thing,
Speaker 5: like how do we get our hands around it? But
Speaker 5: if you look at the last of six years, there's
Speaker 5: a really interesting study by a Meter, which is a
Speaker 5: nonprofit research organization, which tried to look at how long
Speaker 5: it took humans to complete tasks that AI could now
Speaker 5: do and how that's developed over time.
Speaker 4: If you look way back.
Speaker 5: Sort of six years ago, with GPT two, we were
Speaker 5: talking about tasks that took a few seconds that AI
Speaker 5: was able to complete, and we were quite impressed by
Speaker 5: that back in twenty nineteen, and then GVT three a
Speaker 5: couple of years later, was able to do sort of
Speaker 5: ten second task. Then GVT four, which you guys will
Speaker 5: remember that was in twenty and twenty three, you know,
Speaker 5: was able to do sort of five minute tasks, but
Speaker 5: we're now up to the point where we're able to
Speaker 5: do sort of one to two hour tasks with AI
Speaker 5: with a fifty percent success rate. So what you then
Speaker 5: see when you look at it that way is that
Speaker 5: you have this sort of doubling of ability every seven
Speaker 5: months or so.
Speaker 1: What you're actually saying is that AI what could take
Speaker 1: the human two hours AI is doing in five se
Speaker 1: That's what you're sort of saying.
Speaker 4: Yeah, fairly instantly, it's a now.
Speaker 5: So what's interesting about that is like you've basically had
Speaker 5: a model back in twenty nineteen that was like a
Speaker 5: preschooler in its intelligence, that then developed into more like
Speaker 5: a primary schooler and then became like a high schooler
Speaker 5: with GVC four and with GVD five is kind of
Speaker 5: like a genius high schooler that's now winning the Mass
Speaker 5: Olympiad and is able to complete these coding and programming
Speaker 5: tasks that the university level graduates are struggling with. You're
Speaker 5: seeing this evolution of the models that it's kind of
Speaker 5: progressing at about three times the rate of what a
Speaker 5: child would in its development, which is really impressive looking
Speaker 5: backwards when you start looking forwards and you think, well, okay,
Speaker 5: if you take that progression that has been pretty consistent
Speaker 5: for the last six years and play it forwards, you
Speaker 5: start getting into some very interesting, expiking gary types of
Speaker 5: scenarios of what can happen in the.
Speaker 4: Next three or five years.
Speaker 5: And it helps to explain a bit some of the
Speaker 5: what seemed like fairly crazy investments and predictions that being
Speaker 5: made right now in the market of like where things could.
Speaker 3: Go, because at the end of the day, it's probably
Speaker 3: a bit of a winner take hold. Yeah, and I
Speaker 3: know that there are probably five or ten big guys
Speaker 3: literally putting everything they have hoping to be or do
Speaker 3: not know the Microsoft or the Google of tomorrow factor ten,
Speaker 3: But of course there's more applicants than final winner. How
Speaker 3: do you see even the structure of the industry like
Speaker 3: new player, more of the same or some which you
Speaker 3: throw the towel and say, okay, fine, what's the feeling
Speaker 3: right now?
Speaker 5: I think you're right that through the winner takes All
Speaker 5: dynamics is definitely a play part of that is because
Speaker 5: there's a perception there's a sort of a three to
Speaker 5: five year window looking to like twenty twenty seven, eight
Speaker 5: twenty thirty, where if you get the same levels of
Speaker 5: development that we've seen so far, we will achieve some
Speaker 5: sort of AGI, so artificial general intelligence effectively AI being
Speaker 5: on a general basis at the level of the smartest humans.
Speaker 5: If you get to that point, then you start moving
Speaker 5: into a position of potential platform for superintelligence. So one
Speaker 5: of the reasons why you see this dynamic right now
Speaker 5: is because everyone's chasing this dragon, racing to be the
Speaker 5: first to get there or to be one of the
Speaker 5: first to get there, and that explains probably two things.
Speaker 5: Explains the massive investments in compute, and it explains the
Speaker 5: massive investment into talent, and METSA is probably the best
Speaker 5: example of people who are doing this. So the hundreds
Speaker 5: of billions going into computational infrastructure and hundreds of millions
Speaker 5: and billions being spent on talent. The reason for that
Speaker 5: is because if you look at what drives development and
Speaker 5: what will drive these improvements over the coming years, and
Speaker 5: the sort of order of magnitude improvements that we've seen
Speaker 5: over the last six is primarily compute and investment into that,
Speaker 5: and we've seen as sort of a five X on
Speaker 5: Moore's law in terms of computational improvement and algorithmic efficiency,
Speaker 5: and that's where the talent comes in, that's where the
Speaker 5: brain power comes in. Where you have a similar impact
Speaker 5: to computes. Probably it's probably underestimated in the main, but
Speaker 5: it's actually probably simlar to compute where algorithmic improvements are
Speaker 5: improving the performance of the models, improving the efficiency of
Speaker 5: the models, improving their ability to use the compute, and
Speaker 5: therefore improving their performance. So right now you have this
Speaker 5: fight for those two things to move as quickly as possible,
Speaker 5: and because of the trend line, if we can pinue
Speaker 5: on that trend line, then it's a three to five
Speaker 5: year window to get something that people would expect to
Speaker 5: look like an AGI level. That's really interesting in itself
Speaker 5: as well, because then you get this platform twowards well.
Speaker 5: If you're at that level, and especially if AI is
Speaker 5: good enough to do AI research as well as the
Speaker 5: best human then AI can start becoming self replicating, it
Speaker 5: can start producing the next AI model that will be
Speaker 5: beyond a level of human capability or sort of understanding.
Speaker 5: So I'll say it's very exciting and very scaryly very
Speaker 5: quickly when you go down that road. It could also pluitow,
Speaker 5: and there's reasons why it might not develop at that pace.
Speaker 3: I can see that your head is in the stars,
Speaker 3: our boots are in the mud, and we are the
Speaker 3: nuts and balls guy. And a lot of our listeners
Speaker 3: are from the infrastructure world, and of course they're going
Speaker 3: to invest into those data centers, providing equity or that,
Speaker 3: and hopefully the principle of infrastructures is like they like
Speaker 3: to see their money back now. Of course I hear,
Speaker 3: it's fine with German paddocks. We build all of that center,
Speaker 3: we'll have use for them. And by the way, the railways,
Speaker 3: the internet, YadA, YadA, YadA. But when you would build
Speaker 3: a railway, the alway will be there for the next
Speaker 3: twenty thirty years. So even if the trains the first
Speaker 3: three four years were not there, they would come over time.
Speaker 3: The second problem I have is the obsolescence, because I
Speaker 3: see the chips moving so fast in terms of improvement
Speaker 3: that I hear that the data center with chips which
Speaker 3: are three or four years old, you can literally scrape
Speaker 3: it and almost go back from zero. So you've got
Speaker 3: the obsolescence factor in the technology that you didn't have
Speaker 3: in the previous let's say, crazy cycle of bubbles of infrastructure.
Speaker 3: What's your take on my thinking right now?
Speaker 5: I think you're right when you look at the GPU
Speaker 5: side of this, there's definite obsolescence risk. These things get
Speaker 5: replaced every two or three years, so hard to reteep
Speaker 5: that capital spend if you're not using it. What's interesting though,
Speaker 5: right now, I think with ai and I'm not a
Speaker 5: financial expert, but when you look at the annual run
Speaker 5: rates that Opening Eye is operating, that Anthropic is operating,
Speaker 5: and the speed of improvement of those run rates, you
Speaker 5: do see some level of justification even for the numbers
Speaker 5: that they are being valued at and are investing. So
Speaker 5: I think open Aiye was at a twelve billion ARR
Speaker 5: quite recently and was increasing at about a billion a month.
Speaker 5: Anthropic was similar, so they'd achieved like a five billion
Speaker 5: ARR and again was improving about a billion a month.
Speaker 4: So when you compare to say.
Speaker 5: When people talk about terms of the AI bubble competulate
Speaker 5: the dot com bubble, it's like there's very real revenues
Speaker 5: being associated with a lot of the activity that we're seeing,
Speaker 5: which I think it makes it fairly unique. The pace
Speaker 5: of development that's being evident in actual usage. When you
Speaker 5: look at then the levels of investment that they're putting in.
Speaker 5: If you believe in the basic kind of optimistic scenario,
Speaker 5: or you think that optimistic scenario is likely enough that
Speaker 5: there's a decent chance it could happen, then if it's
Speaker 5: someone like Mark Zuckerberg, it makes sense to, as you said,
Speaker 5: drop potentially two hundred billion in investment, even if that
Speaker 5: may not come through, because the risk of not being
Speaker 5: the winner in it is too great. There's all kinds
Speaker 5: of financial shannagans going on around this. There's the fact
Speaker 5: that in Vidia is investing in companies that then use
Speaker 5: this infrastructure, and you've got this kind of circular capex economy.
Speaker 5: You've got things that you guys will understand. But the
Speaker 5: me to be honest, I think in terms of the
Speaker 5: investment vehicles being used, but there does seem to be
Speaker 5: a stronger base to this than other capical investment programs
Speaker 5: that we've seen. What's really interesting for me, I guess
Speaker 5: for this podcast is like the implications through energy and
Speaker 5: the fact that energy really is a bottleneck to this
Speaker 5: development and will be absolutely key to the success of
Speaker 5: being able to build these eighty centers and the way
Speaker 5: that these set companies are now becoming major energy players
Speaker 5: and also quite independent energy players, maybe even separate from
Speaker 5: the grid.
Speaker 1: And it's good to talk about energy. So what I'd
Speaker 1: like to get your view on and just explain what's
Speaker 1: going on here. If I take my iPhone, it uses
Speaker 1: maybe two killer one hours of power over a course
Speaker 1: of a year, and I'm charging it every day, right,
Speaker 1: and I use it over the whole cost of the year.
Speaker 1: And then I go and look at Video's new blackweld
Speaker 1: chip and this uses fifteen kile of what hours of
Speaker 1: power per day and actually the size of it is
Speaker 1: the size of a credit card. So just explain to
Speaker 1: me what's going on there, because obviously if I think
Speaker 1: in terms of relation there, I just can't fathom that.
Speaker 1: And obviously that explains then why there's massive need for
Speaker 1: power going for Explain that, right, what's going on there?
Speaker 1: Why suddenly these chips need so much power.
Speaker 5: That's rather than focusing on the chips themselves, maybe thinking
Speaker 5: about like the size of the data centers that are
Speaker 5: containing them. Obviously, the more power of the chips, the
Speaker 5: less you need for your data center and the more
Speaker 5: efficient you can operate. But the reason that I think
Speaker 5: the data centers are so big, there are free factors
Speaker 5: that really drive AI progress. When is this computational capacity
Speaker 5: and there are order of magnitude imprevements that we've seen
Speaker 5: so far and order of magnitude improvements that can be
Speaker 5: unlocked through these investments. So it's just a huge driver
Speaker 5: of the power that drives then your ability to move
Speaker 5: quickly in AI development. The second is around algorithmic efficiency
Speaker 5: and so the application of brain power to it. And
Speaker 5: the third is the way that these models operate and
Speaker 5: how they become unhobbled by being able to either have memory,
Speaker 5: or to be able to use tools, or to be
Speaker 5: able to operate on an agentic basis, or to reason
Speaker 5: deeply and think the wait that basically the model can
Speaker 5: enhance its own capability through that kind of unhobbling of
Speaker 5: the way that it operates. When you look at the
Speaker 5: power usage of these chips and the power usage of
Speaker 5: these data centers, it's purely a reflection of the fact
Speaker 5: that there is this raw muscle power that can drive
Speaker 5: AI progress. It's the seed at which you're able to
Speaker 5: develop and train models, and then the inference power you
Speaker 5: need to be able to run them, which is also
Speaker 5: massively increasing.
Speaker 1: Myself and Ron probably very much agreed. There seems to
Speaker 1: be just this crazy frenzy in and or around the
Speaker 1: view that we're going to lead not even just gigawats power,
Speaker 1: but one hundreds of gigawats of power to fuel this
Speaker 1: AI race that we're seeing. And we're obviously seeing CEOs
Speaker 1: or businesses, and we're even seeing financial institutions say things like, well,
Speaker 1: we'll never meet this AI gap in our lifetime type stuff. Right, So,
Speaker 1: how do you view this and how do you try
Speaker 1: and help us get to sort of think through and
Speaker 1: understand what's going to happen.
Speaker 5: The way energy is becoming so key to powering these
Speaker 5: data centers is just like transformational, and it's just increased
Speaker 5: so quickly in the last couple of years. And so
Speaker 5: you say, we're now seeing I think of a move
Speaker 5: into the realm of like ken gigawatt plus sort of
Speaker 5: data center clusters as we go into the next few years.
Speaker 5: We already have like one gigawatts in places like Abelene
Speaker 5: in Texas where Stargate's getting developed, or the Colossus cluster
Speaker 5: in Memphis that the XAI is developing, where you've just
Speaker 5: got immense amounts of energy usage that's in the hundreds
Speaker 5: of hundreds of megawatz, just incredibly quickly that's been sput up.
Speaker 5: The difference in these times between tech progress and what
Speaker 5: we know in the energy industry is kind of energy
Speaker 5: development progress, where in tech world you're moving quarter by quarter,
Speaker 5: and energy infrastructure you're moving in many years or decades
Speaker 5: and to get stuff built, and these two things clearly
Speaker 5: don't compute when you try and make these two things together.
Speaker 5: So we now see companies just taking it into their
Speaker 5: own hands to develop their own generation on site completely islanded.
Speaker 5: I'm interested to understand maybe what's happening with permitting there
Speaker 5: and how they've managed to get around rules around this.
Speaker 5: But when you look at the different types of generation
Speaker 5: that they have with options click, clearly there's a big
Speaker 5: investment in nuclear and SMR development that feels like there's
Speaker 5: massive hurdles to cross from a regulatory point of view
Speaker 5: to get new generation SMRs developed that will take ten
Speaker 5: plus years. Renewables are complicated, and they're cheap and they're quick,
Speaker 5: but the complexity of trying to provide base load power
Speaker 5: with them complicates matters. When all you want is on
Speaker 5: demand power if it's completely reliable, which kind of brings
Speaker 5: you back to gas, and obviously at a time when
Speaker 5: the US is swimming in gas. But even cggps have
Speaker 5: a pipeline of five plus years before you can get
Speaker 5: on the list. So they're now reverting to OCGT. And
Speaker 5: so I think in a stargate there's something like ten
Speaker 5: to fifteen of these operating thirty five megawats each in
Speaker 5: XAI facility again talk of thirty to thirty five of
Speaker 5: these are reoperating. So you've got like hundreds of megawatts
Speaker 5: these three or five hundred megawat's worth of open cycle
Speaker 5: gas turbines operating that I guess we'd normally expect to
Speaker 5: only see running at like a five to ten percent
Speaker 5: max load factor.
Speaker 4: That's being on the spaceload.
Speaker 3: Yeah, you're one hundred percent right. All those guys they
Speaker 3: made like all their climate pledge, and we're going to
Speaker 3: be in net Zio and the first thing they do
Speaker 3: is they put and I can even tell you the brand.
Speaker 3: It's all the same. It's the Titan three fifty by Caterpillar.
Speaker 3: And you know what what is great with the your
Speaker 3: administration is, you know andamantal law no onenviromuntalo anymore. You
Speaker 3: want to pollute, please pollute. It is amazing. For the
Speaker 3: sake of speed, the laws don't apply anymore. The only
Speaker 3: thing people are complaining about is when you start linking
Speaker 3: to the grid that it's kind of mixed with the
Speaker 3: great payer and I know there's going to be a backlash.
Speaker 3: I empathize with the necessary speed of development to reach Agi.
Speaker 3: There's a thickness in the way infras being built which
Speaker 3: you almost need to reverse engine. What is the grid
Speaker 3: able to deliver to me? And then start thinking not
Speaker 3: about nuclear because nuclear is going to come in ten
Speaker 3: years and it's gonna cost you three hundred dollars and
Speaker 3: make what I wur now if you're willing to pay,
Speaker 3: that's good enough. It's not gonna be now, and it's
Speaker 3: not gonna be cheap. It's not going to be cheap.
Speaker 3: If you look at the basic numbers. Why do company
Speaker 3: like Oaklow or Fermi who literally have zero project, a
Speaker 3: bunch of slide, not even a product, and there are
Speaker 3: value twenty billion dollar that is for me part of
Speaker 3: the frenzy. And there's going to be some tough awakenings
Speaker 3: because I have no doubt the Microsoft and Amazon they'll continue.
Speaker 3: But there's certain number of things in the supply chain
Speaker 3: which are for real and stuff which are ballooney. The
Speaker 3: question now is, knowing that the limiting factor is the energy, is,
Speaker 3: are they not going to rethink the way they do
Speaker 3: data centers or compute our software all this based on
Speaker 3: this limiting factor.
Speaker 4: I don't think so. Clearly.
Speaker 5: There's efficiencies being made all the time. I know there's
Speaker 5: been a lot of efficiencies made on water usage by
Speaker 5: having close cycle kind of water loops, for example. But
Speaker 5: I think it's a matter of, like everything, it won't
Speaker 5: be a case of, well, we made the efficiencies, we
Speaker 5: need less power. It's just like we've made the efficiencies,
Speaker 5: now we can get more out of that power. They
Speaker 5: will continue to build more and more because we have
Speaker 5: this race that's a three to five year race. I
Speaker 5: guess the logic would be, well, look, it's some short
Speaker 5: term pain. We would like to do this in a
Speaker 5: climate friendly way, but the reality is we can't. We
Speaker 5: can't move at the pace we need to, and it's
Speaker 5: a national security concern, et cetera, et cetera. But you
Speaker 5: know what if we can get to that point in
Speaker 5: three to five years, then the machines will fix this
Speaker 5: for us. We will find out solutions at that point.
Speaker 5: The tech is for the longer term, and this five
Speaker 5: year period will just be a short plit up until
Speaker 5: the point at which we then have a level of
Speaker 5: intelligence that means we can we can solve a lot
Speaker 5: of these problems. And I'm not saying I believe that,
Speaker 5: but I think that would be a justification that would
Speaker 5: get used.
Speaker 3: Excellent No, no, sorry, no sorry, I mean Jiard you
Speaker 3: say excellent, but me I don't say excellent because you know,
Speaker 3: a few take billionaires said you know what, I take
Speaker 3: the cash now, and it's a short term paign. But
Speaker 3: not worry. The future is going to be better. Leny
Speaker 3: used to speak like that, but it was the Communist Party,
Speaker 3: it was not the tech bros. But at the end
Speaker 3: of the day, it's a minority of people who are
Speaker 3: capturing the common good, which is our oxygen, and say,
Speaker 3: you know, the future is going to be great. Sorry,
Speaker 3: in the meantime, I need to pull youute like crazy,
Speaker 3: but you know it's for the greater good. I'm sorry
Speaker 3: you think it's great.
Speaker 1: I don't you can look like that, or you can
Speaker 1: look at it as an opportunity to use the fact
Speaker 1: that what we've got is power the man going up
Speaker 1: for a start, and secondly, somebody who's willing to put
Speaker 1: large amounts of capital to work in energy infrastructure, and
Speaker 1: that gives you the chance to really really innovate. And
Speaker 1: I'm also clear in my head who wins. The winner
Speaker 1: is those who get electricity quickly to market, and so
Speaker 1: that pushes innovation and drive solutions. And for me, if
Speaker 1: I look at it, yes, there's no doubt it's small
Speaker 1: gas engines, but you're put the gas engines besides solar,
Speaker 1: wind batteries, et cetera, et cetera. And you're also going
Speaker 1: to flexibilize the way the data centers work. And I
Speaker 1: think Lron, we've talked about this before, is they're already
Speaker 1: doing this anyway, because at the end of the day,
Speaker 1: when we in Europe and where and during our working day,
Speaker 1: guess what we're using servers in the US? Why because
Speaker 1: it's capacity over there? And guess what we got to
Speaker 1: bed they're using our capacity over here. So we're doing
Speaker 1: this or there is a flexibility here already. And I
Speaker 1: think then we actually actually embrace these technologies to enable
Speaker 1: us to electrify in a better way. That's my positive science.
Speaker 3: Yeah, but child, I agree with you. If the government's there,
Speaker 3: which of course they're not, put some technical and enjunmental constraint,
Speaker 3: they would start being intelligent. The moment is where okay,
Speaker 3: you want to put fifty ocgts. Be my guest they'll
Speaker 3: do it, because that's the easy solution. If they were
Speaker 3: more constrained ehether they would move elsewhere or not known
Speaker 3: in the north of Scotland, or they would put the
Speaker 3: data center where there is load. And now they say
Speaker 3: we can manage our own fleet, so that's number one.
Speaker 3: Number two they would say, we need to put more
Speaker 3: effort in our softwares or our chips or the design
Speaker 3: of our model, so it's much more efficient. And if
Speaker 3: the fact that you can get all the energy you
Speaker 3: want and you know, as polluting as possible, now I
Speaker 3: think that's intellectual laziness. Sorry, we can debate, prove me wrong.
Speaker 5: I kind of see both sides of this. If you
Speaker 5: believe in the way that tech companies do in this
Speaker 5: suite of five year timescale, you can absolutely see the
Speaker 5: need for me quickly, and the only option they have
Speaker 5: is basically gas gen sets in the short term. It
Speaker 5: feels like though the investment potential and the longer term
Speaker 5: investment potential from these guys, with the amount of capital
Speaker 5: they have and the amount of will they have, do
Speaker 5: incredible things to help with the energy transition, which is
Speaker 5: kind of stalling in some ways, I think in the West.
Speaker 5: So the question is like, how do you combine those
Speaker 5: two things together, and how do you put conditions in
Speaker 5: place that force that medium to long term investment in
Speaker 5: cleaner solutions in new technologies alongside shorter term measures. Seems
Speaker 5: to me it's like we're kind of in a singularly
Speaker 5: bad political situation right now to kind of force that
Speaker 5: to happen. Maybe five ten years ago, it would have
Speaker 5: been easier to try and make this trade off work
Speaker 5: and perhaps put conditions around the use of these OCGT
Speaker 5: engines that you have to also correspondingly invest in technologies
Speaker 5: and have a un the comparent some kind of roadmap
Speaker 5: for how you're going to develop your energy production, which
Speaker 5: I haven't.
Speaker 4: Seen any of.
Speaker 5: Having said that, there have been investments clearly in nuclear,
Speaker 5: there's investments into SMRs, there's investments interfusion even that these
Speaker 5: companies are making, how far like those are realistic and
Speaker 5: you see a realistic kind of roadmap of one going
Speaker 5: into the other, or some way of understanding the relationship.
Speaker 4: That's really unclear. It's like really unclear.
Speaker 5: And there's clearly regulatory input you would want into the
Speaker 5: way these investments are being made and into the way
Speaker 5: that these things are being developed that we're not getting.
Speaker 5: And I just don't know how the permitting's being done
Speaker 5: and how the things that EPA in the states, like
Speaker 5: what position they're taking on this or how they're operating.
Speaker 5: I know a lot of stuff is done at state
Speaker 5: level as well. I don't know if you guys have
Speaker 5: any insight on that, but it seems like these things
Speaker 5: have been put up incredibly quickly and probably without any
Speaker 5: oversight on the environmental concerns of it.
Speaker 1: Well, and it just maybe to wrap up, could you
Speaker 1: talk a little bit about what you actually do.
Speaker 5: We try and help companies and organizations adopt AI in
Speaker 5: the right way. I mentioned at the start kind of
Speaker 5: adoption gap between technology progress and an actual usage. The
Speaker 5: whole bunch of things that drive that gap, and it's
Speaker 5: not that the fact that the technology can't do a
Speaker 5: useful things. It's because it's really hard to develop user
Speaker 5: centered products and tools and solutions within organizations, and that
Speaker 5: involves a whole hybrid of skill set. By what we
Speaker 5: do is a business, we bring those skill sets of
Speaker 5: data science, machine learning, engineering, product management, and technical architecture,
Speaker 5: commercial delivery, business case understanding all of these sorts of things,
Speaker 5: and we operate both in the generative aispace. So maybe
Speaker 5: half of our workers a businesses how you deploy large
Speaker 5: language models in useful ways within businesses, how you create
Speaker 5: solutions around them. But also half of our work would
Speaker 5: estimate is more like traditional machine learning type solutions, so
Speaker 5: self built machine learning tools with very specific purpose. And
Speaker 5: actually a lot of our work and energy is more
Speaker 5: in that space. So while we've talked about the generative
Speaker 5: world a lot here is actually that a lot of
Speaker 5: the value of AI within energy sits within the way
Speaker 5: you apply more traditional machine learning techniques to specific types
Speaker 5: of solutions, things like how do you use flexibility effectively
Speaker 5: on the network by forecasting your demand in generation better?
Speaker 5: How do you schedule dispatch of generation in the system,
Speaker 5: how do you manage the you've got like a thousand
Speaker 5: plus balancing mechanism units rather than tens of them. Now
Speaker 5: you've got this complexity to the where you plan and
Speaker 5: run networks, the way you operate them. How do you
Speaker 5: prioritize connection cues when you've got just masses of generation
Speaker 5: and new demand queuing up to connect and trying to
Speaker 5: understand how you prioritize that and design interventions that respond
Speaker 5: to how it impacts your career, a non redistection on
Speaker 5: networks and predictive maintenance in generation, and how do you
Speaker 5: manage customers better as an energy supplier all sorts of
Speaker 5: things across generation, networks and supply where we can make
Speaker 5: things not just more efficient and more productive, but optimized,
Speaker 5: and so a lot of the work is run how
Speaker 5: you make better pocisis when I look at the energy transition,
Speaker 5: and I've been in the space of fifteen odd years.
Speaker 5: Now we're really at that point now where we have
Speaker 5: generation of scale. A lot of the challenges how do
Speaker 5: you integrate the stuff at scale in a way that
Speaker 5: optimizes the way the network operates, where the system operates
Speaker 5: and prevents us from rebuilding and duplicating infrastructure because we're
Speaker 5: able to use what we have efficiently, and I think
Speaker 5: that's what machine learning has just a huge part to play.
Speaker 1: Well, honey, thank you very very much for this world
Speaker 1: one tour of AI being very enjoyable.
Speaker 3: Yeah, sorry for sometimes the level of the debate. But
Speaker 3: I am convinced AI is going to change everything. But
Speaker 3: I see stuff which are not normal, and I don't
Speaker 3: want to talk about the valuation of anthropic or whatever.
Speaker 3: I have no clue, but it's more inside the supply
Speaker 3: chain along the way. I'm always wary that a lot
Speaker 3: of investors are going to lose money because they look
Speaker 3: for the moon but they don't concentrate on the finger. Yeah.
Speaker 5: The fact that is that we're over around in a
Speaker 5: few years time and we can see who was right
Speaker 5: and wrong. So all of these I thought the things
Speaker 5: are gonna get tested exactly.
Speaker 3: Thank you so much for coming one.
Speaker 4: Thanks for having me. It's a lot of fun.
Speaker 1: So what's your takeaway?
Speaker 3: My takeaway is when you discuss with AI, guy, they
Speaker 3: have two or three magic words to make sure that
Speaker 3: you don't really understand what's going on, and I have them.
Speaker 3: The number one is inference. They say, yeah, yeah, I
Speaker 3: like your solution, but what about inference? Of course, I
Speaker 3: have no idea what inference is, so you can of okay, fine.
Speaker 3: And then the second one is Argentic model. First I
Speaker 3: thought it was like Argentic, like but from Argentina or no, no, no, no,
Speaker 3: it's Argentic. And then so of course I had to
Speaker 3: go on AI to kind of figure out it was.
Speaker 3: And it's a totally different universe and they're just rushing, rushing, rushing.
Speaker 3: They have really no idea where they're going, but they
Speaker 3: don't want to be the last, so they throw everything
Speaker 3: at it. And that's what makes the old conversation fascinating.
Speaker 1: What I take out of it, I suppose, is it's
Speaker 1: not a race for AI, because we have a AR
Speaker 1: right in lots of different forms. It's really a race
Speaker 1: for artificial human intelligence. That's really what it's about. And
Speaker 1: there's a mixture of fear, oh my god, what if
Speaker 1: we don't get it and the Chinese get it? And
Speaker 1: on the other side there's also greed, which is if
Speaker 1: we get this, we're going to rule the world, or
Speaker 1: we're going to be the top tech company in the world.
Speaker 1: That's what's going on, and it's driving crazy evaluations. That's
Speaker 1: what I take.
Speaker 4: Out of it.
Speaker 1: Right when I'm looking at I looked at one of
Speaker 1: the utilities the other day and there's the US independent
Speaker 1: power producer and trading at seventy times earnings. I've never
Speaker 1: seen a utility and independent power producer trading it's seventy
Speaker 1: times earnings. You know a bull market. Prices are up
Speaker 1: and you know economy is booming, and maybe at twenty
Speaker 1: times what's seventy times earnings? Mind bo So there's crazy
Speaker 1: stuff going on in terms of evaluations as well.
Speaker 3: Right, okay, job, the big question is is this all
Speaker 3: ai RA is going to reshape the power grid? Or
Speaker 3: will the inertia and complexity of two day's greed will
Speaker 3: deliver some revolution Behind the matter, we'll see how we're
Speaker 3: going to build in America the equivalent of two hundred
Speaker 3: nuclear plans within five years to fuel all that potential demand.
Speaker 3: Very difficult to say, but it's definitely fascinating universe.
Speaker 1: Yeah, Aliston, I want to thank and Andy Perry for
Speaker 1: coming on the show. It was really great to have
Speaker 1: his view on it. And that's very different. No, London,
Speaker 1: let's say maybe the view that we'll have in the
Speaker 1: energy space right totally.
Speaker 3: Well, finally, we'd like to thank the BMW Foundation A
Speaker 3: Berkman for supporting the show and job. I'll talk to
Speaker 3: you next week looking forward.
Speaker 4: To thank you for listening to Redefining Energy.
Speaker 1: Don't forget to rate the show and subscribe on Apple Podcast, Spotify,
Speaker 1: or the platform of your choice.