EP 344 – Is AI Killing Our Push to a Sustainable Future? – Richard Farrell – Eaton

Read the best-effort transcript below (This technology is still not as good as they say it is…):

Michael Waitze 0:12
Let’s do it. Hi. This is Michael waitze, and welcome back to the Asia Tech Podcast. We are joined today by Richard Farrell, the APAC Director of Digitalization, Cybersecurity and Data Centers at Eaton. That’s a big job. Rich. Thank you so much for coming to the show. I really appreciate it. Yeah,

Rich Farrell 0:28
Michael, thanks so much for the invitation. And yeah, the I think I do officially have the largest job title in the industry as well. Though I like

Michael Waitze 0:36
it. It’s a lot of stuff to fall but all the good stuff actually as well, which is great. And before we get into the main part of this conversation, let’s give our listeners a little bit of your background for some context. Yeah,

Rich Farrell 0:46
sure. Look, currently reside in Singapore. Been here for four years working for Eaton, been in the industry. Want to show my age here, Michael, but over 26 years in the technology industry, and about 15 of that has 15 or 16 just in the data center space. In case you can’t tell from the accent. Very Australian. I got asked the other day, what do I consider home, Singapore or Australia? And I’d have to say both. Yeah, it’s very hard to differentiate between both of those. So yeah, loving, loving a lifestyle over here, it’s obviously great to get around to a lot of the countries and I in Asia, I get the opportunity to do that as part of my role, the majority of my role this time, this these days, Michael is really evangelizing the technology space. Obviously, I lead into the data center space, cyber security and digitalization. I run a large team here in Asia. But really it’s been evangelizing and just working with customers to help them deploy their technology solutions, advice on that sort of things as well. And yes, I do get home to Australia as much as I can to surf as much as I can, not much surf here in Singapore. No, really, I do love alternating between both. Awesome.

Michael Waitze 2:00
Okay, in your career, let’s, let’s get right into this thing. In your career. Have you ever seen such a seismic technological shift that impacts businesses as we’re seeing today with artificial intelligence?

Rich Farrell 2:11
Yeah, yeah. And, you know, it’s a well being. I’ve been in the industry. I want to say, yeah, like I said, 26 years, I’ve seen a lot of things. Yeah, packed. I’ve seen obviously, I was around for watch. UK, I was around for the it boom and bust. I was around for cloud as well. And during those times, they were the big things, right? I mean, nothing was bigger than white. UK, we were waiting for airplanes to fall out of the sky. And I remember, ironically enough computers to take over didn’t happen. I would say this would have to be one of the biggest I’ve seen. If it’s, I think we’ll know whether it’s the biggest in the next two to three years. Okay, if I have a look at the fact that this is getting mainstream exposure and major mainstream exposure, and I think that’s really, you know, the barometer, if we’re talking anything in the technology space that makes the leap into mainstream, yeah, outside of industry, media and industry hype, that tells you it’s a pretty big deal. Okay, so if we’ve got everybody in mainstream talking AI, that means it will be one of the major things that will ever happen, probably in our lifetime, in our industry. I think so. Is it the biggest, potentially? Yes, yeah.

Michael Waitze 3:26
I mean, I was on a I was doing a recording with a guy a couple of weeks ago, and he said to me he thought it was the biggest thing since electricity. And I don’t think that he thought he was being hyperbolic, actually, because, again, I don’t remember when electricity first came out, because I’m not that old, but I can imagine when it first happened. People like, Yeah, fine, we can turn on some lights with this thing, right? But once it kind of gets going and has this kind of mass adoption, everything changes, right? And how long do you think that’s going to take to happen?

Rich Farrell 3:57
The change, the change is happening now, if I have a look at what’s happening with the change and the conversations I’m happening, everybody wants to go AI. Everybody’s saying, Yeah, going AI, we’re going to implement AI, we’re going to use AI. I’ve got aI on my phone, but you take it back a step. Why? Why? Why do you want to go AI? When you’re talking to people who want to implement it into banks and, you know, into healthcare, government, wait a minute before you go. Why are you actually going? Ai? Is it because of the hype? And I think at the moment, this is where this sort of goes back to your former question as well, Michael, it’s making that transition now from hype to actually being real. I think so. And whether you believe in AI, whether you believe, whether it is hype or not, is irrelevant, because it’s happening and the massive spend is on so I think a lot of organizations, yes, it is everywhere. It is prevalent everywhere. It is driving a lot of business agendas now as well. But a lot of organizations, and a lot of the individuals and boards who run these organizations don’t really understand why they want to improve. Implemented. I think they just feel they need to, because everybody else is their customers, their competitors as well. But actually looking at what the tangible business benefits are, what the people benefits are, is it productivity? Is it to save an OpEx? Is it to, you know, make more money, save money? That’s a missing piece. I think if you really ask a lot of these organizations that are rolling out, it’s usually met with a, we’re not quite sure, really look it’s, perhaps it’s a little bit too simplifying it, but it’s if we have a look at some of the use cases, definitely use cases in some industries. If we look at healthcare, you know, for example, having real time patient analysis data, and definitely being able, imagine being able to, you know, identify and prevent major illnesses in the moment, rather than having to put the patients through batteries of tests and and waiting time and everything like that. And that’s one of the advantage of AI. We’re seeing that now real time patient analysis, things like automating back end processes for big organizations, particularly on things like, say, debt collection or finance. We can see that in my industry, we’re already seeing the benefits of rolling this out into facilities, teams within data centers, where they can do predictive maintenance for issues before they become an issue. So perhaps I’m being a little blissing saying they’re not really sure why, but I think a lot of the organizations have to have examples that I’ve just given to actually really justify the spend. Otherwise they’re just spending without any sort of tangible outcome.

Michael Waitze 6:36
Can we? Can we talk about a really simple implementation here? If you don’t mind, I was talking to put the team at a company called di D yesterday, and D ID. I think it stands for digital ID. To be fair. I’m not really sure. I didn’t ask the guy, but he was talking about AI generated avatar movies. And you’re just thinking, why do I care about this? Or videos? I should say. But I think from a customer service standpoint, right? Instead of using a chat bot, which I think everybody hates chat bots, right? You’re typing something, and, you know, it’s not a human. It doesn’t feel human. But what this other company is doing is they’re taking AI, and there’s some very complex math around this, right? And they’re taking images, some AI, generated, some yourself, and then adding sort of an LLM layer underneath it, and then using that to do customer service right for FAQs and stuff like that. That’s an incredible use of this, because that means that I, as Michael, can be answering questions for 15 people at the same time, and it all feels to them like they’re talking to me. And we know, I think research bears this out, that if people feel like they’re talking to humans, or if they actually just see a video of something that looks real, their engagement is going to be much higher. That’s a great use of this. No,

Rich Farrell 7:45
absolutely. I mean chatbot, chatbot on steroids, right? Yes, you know a chatbot, instead of just like, blurt stuff out, which you can probably just go and google yourself exactly interacts with you. And I mean, sort of what you’re referring to, there is a little bit of what’s happening in AI. At the moment, we were very familiar, I think, with generative AI, which is essentially chat, BTB, GPT, Bard, you know as well, where, if I have a look at, you know, generative AI, I sort of think about that as being Google and steroids, because really, you are still searching for data. It’s just putting that data in more context for you. Rather than just saying, here’s a link, it’s saying, here’s how you can use it. Here’s how we can justify it, Agent of AI takes it a little bit step further, where agent of AI starts essentially thinking. It’s starting to think about its answers, and it’s being autonomous. So rather than you plugging more data in, it starts to come to solutions itself. That is the real advantage. So what you’re talking about there where it uses both the human element, the human input, as well as its large language models, the llms that you mentioned as well. That’s probably the ideal user case for AI where it goes a little bit wrong is where it’s a little bit too, just more on that agenda of its thinking for itself all the time without the user input.

Michael Waitze 9:03
Yeah, look, by the way, I just noticed I love your T shirt. Skynet, it doesn’t get much better. That is that on purpose, or

Rich Farrell 9:10
it’s not by accident, not by accident. I do. I’ll show you now, Michael, I have a I do always coming onto your onto your podcast, and I do have a couple of presentations coming up in Singapore and Thailand over the next couple of months. And if you are a bit of a tech nerd or a sci fi geek like me, then thank you for appreciating I cannot

Michael Waitze 9:28
get away from this thought. I mean, I saw Terminator movies when they first came out in the theater anyway, and in Japan. Let’s get back to this, though. So when you go talk to companies about actually implementing these types of solutions, right? What are some of the things that, what are some of the challenges that they’re having just trying to integrate this stuff, whether it’s a bank or a healthcare facility or anything like that? Well,

Rich Farrell 9:48
I mean, let’s, let’s, let’s answer this a little bit different way, because we’re talking tech here. Let’s tech for a moment and talk people at the moment who’s an expert in AI, who’s an expert in AI, and banks. I. AI and healthcare, yeah, AI and technology. I mean, AI, in theory, has been around for a couple of decades, but in practice, it’s only been around for a few years, yeah, so, you know, there are, there’s only recently been, you know, college and university courses around, you know, data science specializing in AI. I know that if I go to out to any of my customers, any of their facilities, people, and it people. They’re not AI people. You know, I’m not an AI expert. I’m a person who happens to be educating himself very quickly and talking to customers and AI because it’s part of what we need to do. You know, I’m always skeptical. Michael, I gotta say, and this is at the risk of isolating some of your listeners and guests. Anybody who says, I’m an AI expert, I’m like, Really, yeah, exactly.

Michael Waitze 10:47
It’s like being a Metaverse expert, sure. Like, are we even in the metaverse yet? Like, how can you be an expert for something that just started? Yeah,

Rich Farrell 10:55
exactly that. And it’s, and again, that’s, you know, it’s not often. Sarah has just said it has not been around that long. So going back to your point, where do we find these people that can actually, yeah, not just, you know, not just do the large language models. There’s actually plenty of programmers, developers out there that can do that. What about the people after use this day to day? And obviously we talk about, you know, the whole AI versus people and taking jobs at the end of the end of the day, you’re still going to have people in the background. They’re going to be in these facilities. Where do we get them? Where do we get these people? I mean, for me, that’s going to be a big challenge. And a lot of organizations diving into AI, but then they need people on the operational side to actually run the AI, you know, as well, and understand the AI Exactly. What if something does go wrong? What if you you suddenly need, what if your algorithms goes a little bit left of field, you know? What if there’s like, 10,000 lines of code that need updating? You know, there’s also the compliance. And compliance is something I think that is lacking majorly at the moment. If you have a look at in Europe, I mean the the EMEA area. So Europe, Middle East, Africa, that sort of area. But Europe in particular, are usually a step ahead of everybody in regulation and compliance, things like GDPR, environmental compliance and data centers as well. They do now have a compliance and regulation for AI. It is a about 130 30 page document that I’m happy to talk to your readers about, but not on this podcast, because we’ll all go to sleep. But the main point is they have got regulation in place now, but that regulation is for EMEA. What happens for the US? What happens for Asia and the rest of the world? So I think these things outside of the tech itself, the tech is there to be used as any number of organizations, depending on how you want to utilize it. You can go to but the people and the compliance and governance part that’s there’s a big gap there. Let

Michael Waitze 12:51
me share some experience that I had when I was in Morgan, Stanley, Goldman Sachs, sitting on a trading desk. We started implementing algorithmic trading 20 something years ago, and it actually changed the way we started hiring people. Because if you were sitting on a trading desk and like you said, the algo started to go haywire and you didn’t know what to do, you had a problem, because you really had to understand how it worked. And what ended up happening is on the trading desk, we started hiring like physics and mathematics PhDs to actually run the trading desk, because they were just way better at it. I’m wondering if you think this is going to happen. Is going to happen in your industry as well, where, because people are going to have to have this embedded knowledge that you’re going to have to hire different people to do some of these things, and I agree with you, people are never going to go away. And we can argue about this, and I don’t think you would disagree with me until we’re blue in the face, but this type of disruption always creates opportunity. It never scares me. I think you and I are around the same age, and I think we’ve seen enough cycles to know that every time someone gets scared about this particular technology is going to put millions of people out of work. It just never happens anyway. Do you think this is going to change hiring as well?

Rich Farrell 13:53
Yeah, you know what that’s that’s a really good point. And something, something I haven’t actually thought about much. That’s a great question. If, I guess, let me look at the cyber security side at the moment, because one of the things we really notice in cyber security is there’s two different factions in each organization. You have, the IT guys and the IT guys. Cyber security is still usually, you know, at the device level, the desktop, the phone, that sort of thing, yeah, service, you know, inside the data center, the facilities, people are looking at cyber security, looking ot operational technology, not it. So they’re looking at things like the the diesel generators and the batteries as redundant power as a cooling systems as well. You don’t have facilities guys who understand a lot of it and a lot of it. Guys don’t really understand how a chiller or a cooling system works inside of a building. Yeah. So a lot of time what we have to do, and we’re talking cyber security, is trying to bring the the OT operational technology into the it Information Technology conversation, you know, at the moment. So if I can use that example, back to what you said around around AI, the. Then, and your example around the training floor, when you you basically had people with maths degrees and physics degrees help and run the training floor, there’s going to be absolutely a need for that. And, yeah, goes back to our previous point around where are the people at the moment, you know, the the facilities guys are not AI people, the IT guys probably are the most likely to be it, but not it, you know, at the moment. So there is it’s going to be after a whole new, different skill set, you know, implemented there. Which brings us on to another interesting point around the lack of talent at the moment. You know, in our industry, overall technology industry, the technology industry, moves so fast that talent has to try to catch up, and that’s the real risks that we actually have. Yeah,

Michael Waitze 15:43
I hear this all the time from guys and gals that are in the IT services business. They’re like the people that we have are amazing, and they’re super great at maintaining the systems that are currently out there. But we need people that know the new stuff too, and that’s becoming really challenging. You’re right, because it’s just moving so incredibly fast. Let’s talk about this, though, too. Ai, just like every new technology uses uses servers and power in a way that other technology hasn’t. It’s not like we haven’t optimized for that yet. How do we handle this kind of balance between we really want to use as much of this as possible and it we want to be as sustainable as possible as well.

Rich Farrell 16:22
It’s so this is my this is my pet project at the moment. Go for it. This is what, this is what I’m writing about in the moment. I’ve written several articles about this. This is basically what I’m presenting on each of my presentations, and what I’ve been interviewed about these days. It is such a juxtaposition between saying, hey, we need to roll out AI which, which basically means that we have to increase power to, you know, maximum levels. You know, basically to to be running these, these GPUs and these chips that actually need all this power, yet we’ve committed to being carbon neutral and green and sustainable by 2030 here’s the problem with with with AI and sustainability the crux of it all, when these big organizations, and I’m going to, I’m going to use some of my customers as examples, please. You know, again, without throwing my customers under the bus, because they are my customers as well. But the big hyper scale organizations, AWS, Google, Microsoft, better when they committed to these green targets and these sustainability targets, I wasn’t a thing, no sort of a thing. And a lot of these organizations committed to these at the beginning of the decade around about 2020 for whatever reason, organizations, including my own, they really lock around numbers. So they said, by 2030 will be this. It’s 2020 now, by 2030 will be this. But AI didn’t really come around until 2022 into the industry. And all of a sudden we started redesigning how we do data centers. We need more power. We need more redundant power. We need more water, you know, as well. This is, this is another issue, which I’m sure we’ll talk about on the sustainability part of it as well. We did all these extras, but yet we’re trying to meet our sustainability targets that we set two or three years ago. Now, if you look at all the figures, and I know that you’re not a fan of stats or no, go for it. Go for it, you know. But if you have a look at the spend and the increase that’s happening, okay, you have some of these hyper scales that have already publicly said, and this is where I do take my hat’s off. Particularly Google and Microsoft, they are being very transparent. You know about this? Which is, which is what we want. They’re saying, Hey, we’ve had increases of 30 and 50% in our power requirements, yeah, because of AI, and that’s going to make it very difficult for us to hit those 2030, targets. Now, the reason why I’m saying hats off to them is because they are being transparent. They’re not trying to hide it. And unfortunately, some organizations are. They are not trying to hide it. They’re actually trying to be very open about and saying, Well, how do we do this now, Michael, if Microsoft and Google and all the experts there that are you know know more about this and what you know you and I do, then that’s an issue. If they’re trying to struggle, to say, hey, we need to build AI. There is a need for it. We’ve got investment. Our customers want it, but we’re not going to hit our 2030 targets. We’re not going to hit it before increasing power by 30 or 50% Yeah.

Michael Waitze 19:11
I mean, how is it possible, right? Even Apple says that they want to be net zero. It’s not, but, but they’re rolling out AI. They can call it Apple intelligence, whatever they want to over a billion devices right in in both their new iPhone 16 and also the old ones that have the GPUs or, I mean, sorry, this the processors that can handle it. And there are billions of these devices out there, so something has to give, yeah, 100%

Rich Farrell 19:35
I mean, I’m here it is right here, bit of shameless branding that I get sponsored by Apple. I’m sure you don’t either. This has got neural AI. Neural AI came out in the in the last iOS, yep. So it’s already running AI. You know, we’ve got it. I’ve got two iPhones either side of I’ve be here as well as the watch. So it’s down at the device level. And this is the thing, right? It’s really these things that really generate all the. Data and drive these things really drive the technology business. At the end of the day, lots and lots of data, that data goes in the cloud, that goes and lives in data centers. So you’ve got all that happening already, let alone all the extra software on top of these, and all the extra AI applications which generate even more data as well. Yep. And you think we you think about the worst thing? I don’t know about you, Michael, but I have, well, they’re now three young adults, but three teenagers. The worst thing could happen in life is if there was no Internet, that was a disaster, that was the worst crisis. You know, in the world, everybody’s getting more and more data. Everybody’s generating more and more data, and if we don’t get that data instantaneously, then we’re not happy. On top of that, we now got aI data centers, not just these ones for normal data AI data centers. On top of that, the majority of us, and I won’t say all of us, say, Hey, do we need to do something about climate change? Two years ago, I would not be able to get a start of any presentation if I didn’t headline with sustainability, right? Today, I don’t get a headline any presentations. There was a headline with AI. So it’s almost like we hey, we’ve got a problem here, but hey, ai, ai. It’s all very shiny. We’ve sort of forgotten about the

Michael Waitze 21:14
sustainability part of it. Yeah, well, I just want to corroborate this story you’re telling about the expansion of data, right? When I was a kid, we had a camera. You could get a roll of film with 1236, or maybe 424, to 36 pictures on it, you’d go and get it developed. But that was it, and it was kind of the only thing you did with it, unless you had your own dark room, right? And then, when was it? 10 years ago, maybe more Instagram came out, and then you had Instagram filters. It was pretty straightforward, right? All that processing could happen on the phone, but now it’s like Insanity. My daughter, okay, you have children. I have kids as well, but my daughter now photographs or videos, everything that she does. And Apple is adding all of this new functionality to the cameras, along with Android that does processing on it, that, again, is AI generated, that changes things, enhances it, fixes it, all this kind of stuff in real time. And you’re right, all of that is sitting in a data center somewhere. I want to know, like, how these data centers are getting redesigned? You did mention it more water, more power efficient? How are these stuff things getting redesigned so they can better handle all this stuff? And can we over anticipate, like, how big this impact is going to be. Do you know what I mean? Because if we just do it for today’s stuff, it’s not going to work. We have to anticipate what it’s going to look like five to seven years out so we can make it better for then as well. Yeah,

Rich Farrell 22:30
yeah, exactly that. And that’s that, that latter part of what you mentioned is what is, what is missing, yeah, now where we’re all worried about sustainability, and our could be more sustainable, and now sustainability, it’s still there. It’s still second or third priority, but it’s a distant second or third compared to compared to AI. Look with the different designs of data centers was happening before AI, and a lot of it was sustainability driven. So people were looking about how we can actually make our data centers more efficient and effective. And there is a couple of metrics that we use in the data center market. One is called P, U, A, P, so that stands for power usage effective. The standard power usage efficiency, okay? And the closer you can get to a number of 1.0, that means the amount of power that you’re using in the data center is 100% utilized by all the equipment in the data center. That’s perfect. So 1.0 is where you want to be before AI a top tier data center PUE would be considered around about 1.3 so that means you’re getting about a 30% loss. But that’s acceptable, because you need, you’re going to have that of the UA. And it actually didn’t reflect that much on, you know, on overall balance sheets or anything. If you have a look where sustainability came in and green targets came in, they wanted to lower that even more. So you had things like battery, energy storage that people wanted to replace diesel generators with diesel. Obviously not great for the environment. You had things like grid what they call grid aware devices. And grid aware devices are basically devices that can actually sell power back to the power grid or take the entire facility off the power grid as well, you know, during peak time. So those discussions were already happening, and they were happening because there was more and more of an increase in power because of all the data that we’re generating as well. Now, when it comes to AI, it’s people, we are trying to organizations, and people are trying to integrate things like renewables, you know, into into our power infrastructure. Nuclear, nuclear is very much back on the agenda now to power data centers, which was unthinkable, you know, even four or five years ago as well. So these things were already happening. Michael, well, you’re already thinking about how we can make data centers more energy efficient. But I’d have to say the in the race to become number one in the in the AI market, in. Cloud and data center, market supporting AI, the sustainability piece is being left behind. Yeah. I mean, it is. It was already being used, but it’s look, it’s just unfortunately, like I said, it is a distant second.

Michael Waitze 25:13
Can I ask a kind of a funny question, if you don’t mind, sure, can AI itself and sophisticated mathematical models, along with like some properly placed IoT devices, actually help solve this problem for itself, particularly when it comes to the power usage efficiencies that you were just talking about. You know what I mean? So it’s constantly optimizing, because along whatever the power grid that’s being used, there are devices there that say this should actually be redirected over here, or that type of stuff,

Rich Farrell 25:41
all right, so now I’m suspicious, Michael, that you were somehow looking over my shoulder when I was preparing my PowerPoint presentation. I’ll be doing exactly on that. The problem is the answer. Say again, problem is the answer. The problem is the answer, yeah, go ahead, you know. And I think, I think there’s actually probably a quote somewhere, I’m sure I’ve read that somewhere, the problem is within the answer. I’m definitely not the first person to come up with that. So AI itself. You think about what AI is. It’s a bunch of algorithms to make things more efficient, to make things more effective, to get things done more quickly, safely, you know, as well, that’s exactly what we needed in inside data centers and in the cloud, like we mentioned before, we all want data instantaneously. We all want answers from the internet instantaneously. We want them to be as informational and as accurate you know as possible. And then, if I’m running a data center, I don’t want anything bad to happen, right? Anything bad is going to happen. I want to know ahead of time. So we already have software that runs inside these data centers. It basically monitors everything, but it’s very much at at that software level. So it’s monitoring and reporting pretty much the temperature in the data center is too high. There’s a water leak in room, whatever the is a power surge on this part of the building. Whatever it may be. The next step from that are things like you mentioned, IoT, sensors and predictive analytics. Now Predictive analytics is again, one of those, you know, sciences out there that is very specialized, but essentially, we’re trying to predict a problem before it happens, which is ideal? Yeah, the power that you can actually put behind that with AI, it actually solves a lot of the issues a lot of customers that I talk to have at the moment, all of them are at the software piece. You will not walk into any data center without them having software monitoring, but it’s very much at the monitoring level. AI itself could actually be the answer that does the predictive analytics piece and pretty much automates a lot of the functions of the data center as well to alleviate a lot of those

Michael Waitze 27:47
issues. Yeah. And then again, it gets back to this hiring thing, like, where do we find the people that can actually write those algorithms that understand how this stuff works? But it’s, but it’s a good it’s, in a way, it’s like a great problem to have look. When I was working at one of the big banks in um in Tokyo, I asked one of the technologists there, like, why don’t you quit? And like, start, do your own startup. This is kind of at the beginning of, like, the internet boom in early 2000 and he said to me, great technologists like to solve big problems, and banks have, and he didn’t mean this in a negative way, but he said banks have lots of things that can be solved, and they’re big and complex, and I want to do that. And I thought that was kind of cool. And I think it’s the same thing here, that these are big things that need to be kind of solved. And great technologists are going to want to look at these problems and think I can solve them and try to go for it. Yeah, yeah.

Rich Farrell 28:33
Look, that’s a great point. You just said the technology, the technology is actually there too. Yes, yeah, these a lot of these issues, right? Whether it be hardware or software. I mean, I talked on sustainability, I said battery, energy storage, grid away, devices, they’re real. They’re here. Now, you know, we can, we can go and sell those to customers, and we can show you the tangible benefits. And the TCO total cost of ownership over X amount of years. The software is there, the AI is there. What a customer needs is the willingness to actually go and go and do it, and the willingness sparkle still comes down to dollars and cents. There’s going to be a premium to implement some of these now. You know that capital expenditure to get that operational expenditure benefits in the future.

Michael Waitze 29:15
So what has to happen here? I’ll let’s leave with this right? Because I think it’s kind of on a more positive note, what do we have to do to convince people that, like you said three or four years ago, if you didn’t have sustainability in your presentation, you wouldn’t get any headlines. And now it’s AI, and that sustainability piece is kind of falling apart. But if you give a presentation that has the conversation that we’re having right now with AI, with this sustainability and how those two things interact. What do we have to do to get people to pay attention to that as a partnership, as opposed to two sort of diverse forces?

Rich Farrell 29:52
There’s two parts to it this. This is actually the crux, really, of the conversation when it comes down to how do we bridge these together, and a conversation that I seem to be having. More and more often. Yep, you know, first of all, there has to be, as I mentioned, there has to be a willingness to to understand and go in, to want to make the investment to make it work. You know, AI in isolation is not going to help you hit the sustainability targets. Likewise, having a completely sustainable data center is going to be so cost prohibitive if you want to roll out AI at the moment, in the future, you know, as demand increases, costs will come down. But you know, at the moment, and that willingness really has to come from the most senior level, whether that’s the board, the CEO, the business owner, you know, whoever it may be. I mean, it’s no no surprise, and no secret, there’s a lot of private and public equity firms that are now major shareholders. A lot of data centers out there. Why are they major shareholders? There’s a lot of reasons, whether it be for property, bigger return of investment, you know that they get from other areas, but they know the boom is there, okay? So you’ve got to, even at that level, explain, hey, you know, in the long run, it’s going to be a lot more beneficial for you if you have the willingness to invest in some of this renewable and sustainable technology to drive your AI data center? Yes, there might be a cost premium on traditional build, but without that, we’re not going to meet our sustainability targets. It’s just going to be empty rhetoric that’s nice in an annual report, but with no data to actually go behind. So the first one is willingness from the most senior level. The second one, which we’ve touched on, you know, quite a quite a few times during our chat, is it’s not about technology. Again, I reiterate that technology is there. It’s about the people. Where do we find the people? How do we train the people? Where’s those integration of skill sets? You know, again, to your example of working on the training floor. And suddenly there’s a host of, you know, mathematics professors and PhD walking in to help you. Help you guys out. How do we almost have a hybrid skill set of IT facilities, you know, AI people, you know as well. How do we actually do that? The answers? Sure. We always answer. Always ransoms, always got told, never have a problem without an answer. We have to try to look at this at the ground level, which is real. Ground level is really from the high school age into university and tertiary education and and trying to work from down there. And it’s definitely, we’re definitely able to cross skill as well. There’s existing skill sets out there that can make the leap into this, but it’s got to come from both directions, from the highest levels to the people just getting into the industry.

Michael Waitze 32:24
Awesome. It’s the perfect way to end. Richard Farrell, the APAC Director of Digitalization, Cybersecurity and Data Centers at Eaton, a lot to do very exciting stuff. Thank you so much for doing this. I really appreciate your time today.

Rich Farrell 32:36
Love the chat, Michael. Thanks so much.