← Energy Gang
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- [00:02]
A
Hello and welcome to the Energy Gang, a discussion show from Wood Mackenzie about the fast changing world of energy. I'm ID Crooks and welcome to a special episode. We're going to be talking about understanding the connected world of energy. The reason for this is that we at Wood Mackenzie have just published a new book which is called Connected and it's written by our chief executive, Jason Yu and our chief analyst, Simon Fryers. You can actually download it from our website, woodmac.com so please do do that. Go ahead and check it out.
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Wood.
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What it's about is about the key challenges facing the energy industry today and what we see as some of the solutions. And to talk about that, it's a great pleasure to welcome Jason Liu, who as I say, is our chief executive here at Wood mackenzie. Hello, Jason. Welcome to the show.
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B
Hey, Ed, it's great to be here. It's a pleasure.
- [00:45]
A
Yeah, thanks very much for joining us. And it's also a pleasure to welcome to the show for the first time another new guest. Sunayana Ojalan. Sunayna was until recently the senior director for corporate strategy and climate change at hesse, the oil and gas company. And, and you've recently started a new job, haven't you, at the investment company Bernstein. Hello, Sunil, welcome to the show.
- [01:04]
C
Thanks, Ed. Thanks for having me. Hey, Jason, it's really good to be here. Yes, I'm excited to say that I'm going to be an equity analyst for Bernstein in the energy and energy transition sector.
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A
Fantastic. Yeah. Well, great that you're able to join us and great to take part in this conversation. I think that's a fantastic perspective to share this discussion we're going to be having about the future of the energy industry. One of the things we always like to do when we get people on the show for the first time is talk to them a bit about their careers, how they first got interested in energy and how they got to the roles they now hold. Maybe, Jason, to start with you on this, as I say, what attracted you to energy and what was the pathway that took you to being chief exec at Wood MacKenzie?
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B
Oh, great, Ed. You know, I just would probably start off and say that I think energy's been a bit of the family business. My father was a civil engineer and actually a wind expert. And so in my youth we would actually spend vacations touring wind farms. You know, I think we, you know, as younger kids would have preferred being on the beach or amusement parks, but we'd be touring wind farms. So I guess that's where it all started. I will say over the last 30 years, I've had an opportunity and a pleasure to run some of the larger software and data companies in the world. And you know, I did have one stint where I ran one of the largest energy software companies in the world, Allegro. And now I've been at Wood McKinsey now for about 15 months as CEO. And the only thing I would add is I think at Wood McKenzie we have this fantastic 100 plus year history, but I'm the first outsider from Wood McKenzie, but also the first one with a tech AI background. And I think that's part of the outsider's perspective that I think I could help bring to the energy world. Is that kind of how does energy meet tech and AI?
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A
Yeah, absolutely. That is very interesting. And as you say, very much traditionally at Wood MacKenzie, we've appointed CEOs from inside the company. You, I think the first one in the whole history of the business to come from outside, as you say, really interesting to bring that tech perspective. And that's definitely something we're going to be talking about on this show. Before we get to that though, Sanayna, tell us about your career. So you spent a long time at Hess, didn't you? But what's your journey been?
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C
You know, I was reflecting on this, Ed. I actually entered energy by accident. It was early 2000s and I found myself with not one, but two electrical engineering degrees at at a time when tech was crashing, Slumbershay was hiring. It was not a company that was on my radar, but oh well. And in hindsight it was fantastic because it provided such a great foundation for understanding how complex and how large the energy system can be. I got into strategy through business school at Rice and did six years in strategy consulting in Boston before joining Hess. And at Hess have done various roles in onshore and offshore before joining corporate strategy and doing our climate change strategy as well. So I've moved around a lot, joined energy by accident, but stayed by choice.
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A
And then getting into energy equity research in particular, as you've been saying, that's kind of a new thing for you. So this is a new field for you to explore.
- [04:09]
C
You know, it's more of like a shifting perspective for me. At Hess, I had the chance to shape energy and climate from the inside and now it feels like a natural next step because at Bernstein, I'll be analyzing the sector, a sector that I love from the outside through more of the market lens and more of the companies, you know, energy transition. And I'm sure we'll talk about this as well is at such a critical point and I'm excited to bring my background in engineering strategy as well as climate and sustainability to help investors and companies really cut through some of the noise that's been there in identifying the opportunities as well as the risks that lie.
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A
Right, Absolutely. So let's start off then by talking about the shape of the energy landscape. Maybe. Jason, get your thoughts on this then. So as you say, been about 15 months leaving wood Mackenzie. When you think about the energy industry as it stands today and the challenges that it faces, what do you think are the biggest issues?
- [05:08]
B
Obviously I wish my partner Simon Flowers and my co author of the book had an opportunity to also chime in. But I think the way we laid out the context of this question in the book was a substantial kind of deep dive into why has the energy landscape changed so dramatically, particularly in the last five to 10 years. There's an incredible amount of additional complexity now, whether it be now the rise of energy demand, energy transition or climate issues, coupled with now regulatory involvement and other pretty substantial changes around national security and others. And that has created a lot of additional complexity. And what also is kind of added to the mix is enhanced amount of volatility. And so in the last year plus, myself and Simon have gone out and met with several hundred of our largest energy customers and we met with the CEOs of these large energy providers and we definitely started recognizing some patterns and almost like a zeitgeist that was happening in the industry, which was there was just a lack of predictability going on in the industry. And we heard that from multiple CEOs that felt like they can no longer predict the future. And we're not just talking about 5, 10, you know, 15, 20 year outlooks, we're talking about predicting over a six month to nine month period of time. And we ultimately came to the conclusion that the historical ways in which strategic planning and operational planning had to be fundamentally rethought, in fact they arguably had been broke because of this volatility and complexity. And so the book is all really lays out the reasons why we ended up in a situation where the old paradigms don't work. And then our proposed solutions on what the new paradigm should be for planning and decision making, both strategic and operational.
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A
Yeah, I think that's really interesting and I do think, you know, people sort of seem to always say, oh, everything's very uncertain and things are more uncertain than they've ever been and so on. But I do think there are real grounds for thinking that the uncertainty today is higher than it's ever been. As you say, a lot of different kind of interrelationships, interconnections that didn't exist before, that exist now. The whole issue of climate, as you say, which is something that people didn't really think about in the energy business 30 or 40 years ago, is now very present still, despite everything and then threw into that AI and what's happening, this kind of brand new world changing technology which is kind of evolving at such a fast pace. As I say, I think it's not just an empty thing to say, to say uncertainty is greater than ever. I do think there's something really in that.
- [07:43]
B
Absolutely. I think Ed, just to add, I think on a couple of the vectors that you mentioned, first of all, with the AI demand, I've been in countless customer meetings just recently and one of the obvious questions is what is AI demand going to be? Is it going to be the hockey stick increase or is it going to be more tempered? And with Wood McKenzie, we're obviously doing everything we can to generate that type of output, whether it be our sensor data, but also when you look at things like congestion cues and interconnection cues, there's a lot of ways that you can try to estimate that information. And I think those are some of the techniques that we would say helps bring a little bit more clarity to some really important questions because the impact is substantial when you're talking about that type of increase in load on our grid.
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A
Yeah. As you say, any then insight you can bring into that uncertainty is then kind of particularly valuable. Now what do you think then when you think about key challenges in energy right now, what are the big ones that stand out for you?
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C
Yeah. Looking at it from the strategy perspective. So inside a corporate perspective, one of the things we used to deal with is. It's exactly what you said, Jason. Basically the energy sector is so different because you're dealing with these four forces, essentially geopolitics, policy, tech and then the regular consumer supply and demand. Right. And all of these four forces are interacting constantly. And so you could have a breakthrough in tech, but if policy shifts, then the economics might not work. Right. Or if consumer demand shifts, kind of like how we're seeing in EVs right now, you have to start again from forecasting oil demand for the long term. Right. So that's one of the inherent challenges that companies are working on. And so really one of the solutions is to think about, you know, how do we think through exit ramps so that we can pivot quickly when we find the information changing. I think an inherent other, more so for oil and gas companies challenge is that it's a depletion business. So every barrel that we have produced was our best barrel. Right. And we have to replace that barrel somehow. So fields decline naturally about 5 or 7% a year. And if we're not constantly reinvesting your production and your cash flow shrinks. And so do you go find more resources? Do you buy the resources? How do you actually allocate that capital? And then two other challenges that I'll highlight in the energy space is the time horizons. Not for every part of the energy space, but largely for infrastructure as well as, you know, offshore fields or pipelines, things that you were talking about. Jason, the time horizons are long, so you're making a decision thinking about a certain future and that future might not exist when you're coming to fid. Right. And then finally in the commodity space, we're essentially price takers, we're not price setters. And that's the, the last part of that challenge. So it's an interesting dilemma to maybe.
- [10:46]
B
Add what you're saying, just some real life customer stories. One is we actually did some analysis recently on some of the European majors and some of their obviously recent, over the last three to four years, decisions to obviously move into the power renewable space away from some of their core competency in oil and gas. And unfortunately there was obviously some less optimized decisions that were made. And we actually calculated the exact impact and it was roughly about $80 billion in lost value with kind of suboptimal decisions in that, you know, in some of the, the investments they made. But also I just was, was out misbetting with one of the largest developers in the US and, and the CEO. And you know, one of the biggest issues that they're dealing with is obviously with the changes in the big beautiful bill, you know, what does that mean to them, real time from an investment and planning perspective and even to a point now where they're considering options as gas. You know, both trying to understand the price of gas because it has an impact on power, but also, you know, gas powered power plants obviously have a big potential impact on AI generation. And so they need to understand that piece as well to make better decisions. So the world has obviously gotten a lot more complex and, and the volatility is much more substantial than, you know, five, 10, you know, 20 years ago.
- [12:04]
A
Yeah, that's really interesting and I want to pick up on something you mentioned, Sona earlier You talked about the energy transition. You just mentioned the energy transition. I wanted to kind of just interrogate, I think, as they would say in academia, that use of words. Because as you say, Jason, if you look at some of the decision making that was taken by European oil and gas companies over the past kind of five to 10 years, clear value destruction there, as you say, that figure, $80 billion. And I think part of the reason for that was that kind of conceptual framework about whether there's a transition happening or not, in the sense that there was a kind of an inevitable transition and a progress away from fossil fuels towards low carbon energy, particularly towards renewables. So you could say, oh, well, the words we use, whatever we call it, don't particularly matter. But on the other hand, as I say, I do think it kind of creates a mental framework that then actually helps guide a lot of decisions. So, Jason, how do you feel about that language and do you like to talk about the energy transition? Is that something you think is a useful kind of mental framework for what's going on?
- [13:07]
B
I think what the way we tend to look at, and this has been pretty consistent with Woodmac for the last five to 10 years is I do think Woodmac has consistently said that we need to every ounce of energy we can get. There's going to be constant need. There's an insatiable appetite of new things that need new energy. You've got still 7 billion people that are below kind of modern usage of energy, roughly 2 billion people that are still using firewood right now as their main source of energy, and then an additional 3 to 5 billion above that that are still not able to use full optimal amount of energy or electricity to power their daily lives. And so this demand is obviously coming online in coming decades and will really serve to drive further increases in energy demand. And so we've increased. We started thinking about the word energy evolution, that it's not a light switch, it's more of a dial, if you will, and that there will be continued usage of oil and gas products for many, many decades to come. But also we need to see more renewable type generation going on as well.
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C
Yeah, no, I agree with that. I think over the last five years, maybe five to 10 years, the thinking has evolved. Right? And so energy evolution, energy addition, whatever you want to call it, energy growth is tied to GDP growth. And so if you break that out into how some of the myths have been busted over the last five years, it's kind of like what you said. I think people have aligned on the fact that it's not a switch and you can't just switch off hydrocarbons. Right. But if you take every commodity. So oil demand has slowed down or is slowing down roughly 1 to 2 million barrels a day growth over the last decade. It's probably looking at like 0.5 million barrels a day going forward. Right. So it's growing but slowing. Gas demand, obviously with everything you've mentioned on AI, but also on shoring of manufacturing, electrification and more just cooling needs with climate change. That's gas demand. There's, there's a ton of gas bowls out there as well. And then on the new energy side, I think it's interesting to think about what are some of the near term technologies that are going to work and then what are some of the technologies that are on the horizon that are probably more like a 2035ish sector. So for instance, solar, wind are obviously growing. Battery storage is great and is needed if we want to use more solar and wind. And we can talk about all of these as well. I think some of the capital that went into the clean technology side of things was probably chasing hype, was probably learning as well. So green hydrogen for instance, not sure where that's going to go. But then nuclear is definitely going to be needed. Right. And that'll be more of a 2035. So I think there's more of an understanding of the fact that energy is really complex. We're going to need all sources. Some are more near term and economic right now with and without subsidies. And some are more of the long term, mid 2035ish think in many ways.
- [16:15]
B
We tend to think of the world as energy addition or energy evolution. And one of the reasons is kind of the balancing of those four really key pillars. I think unfortunately there was kind of an oversteering on one of the pillar which was climate. Climate is super important. But when you look at energy demand, it's not only AI, it's also the 7 billion folks in this world that are living in levels of poverty and trying to bring them up to a higher quality of life. But even when you look at regulatory, I think we all understand now that national security has become a big component of that regulatory piece. And so now I think what's really complicated the matter is trying to balance those four areas. And the other thing I would just add about what's made energy transition a little bit more, I think less of a relevant term is the integration of all the energy components. They're all integrated. It's not, you know, it's not siloed. And I think the energy transition view was everything's kind of siloed. And now I think everyone sees that the impact and the interdependence of each of the energy generation capabilities or oil and gas is all interrelated. And so we've got to think about it in a much more kind of holistic, integrated view rather than as in separate piece parts.
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C
And I'll just add to that. I think there's regional differences as well. Right, Jason? So, for instance, I think, you know, the over indexing of climate probably was more of a European side of things. China is definitely focused on energy security. Right. And so it's looking at a lot of the critical minerals work, A lot of the magnets that they're building or manufacturing capacity that they're building is more from an energy security perspective. The US has a really interesting opportunity having resource, being resource rich and also being having companies that are responsible producers and so wanting to do the right thing on the climate angle as well. So I'll just add that there are regional differences there.
- [18:07]
A
Yeah, no, that's very true. That's a really important point. As you say, those differences are very significant and persistent. Just going back to something you were just saying, Jason, very much resonated with a lot of the conversations I've been having. You said you were talking to people in the US about, for instance, integrating solar and gas. And that seems to be a real kind of buzz thing now. Just I've lost count literally of the number of people that I've been having exactly that conversation with in the US just over the past couple of weeks. In terms of apparently the new buzz phrase. I think it's the integrated energy plan. But this is a thing that a lot of people now are interested in, as you say, these kind of developers who up until very recently, probably, you know, up until kind of nine months or so ago, would have been very strictly kind of solar and wind and storage focused, are now saying that they need to offer natural gas as well as part of the package, given what the market wants, given what the policy environment is. Now this is something which absolutely makes sense, as I say, in terms of where the market is moving. Not always easy to deliver, particularly not if you're not super experienced in that field and you're kind of learning about it for the first time. That's just one very interesting little micro example, as you say, of not so much sort of, you know, transition and kind of leaving one thing behind and moving to something else, but actually Kind of trying to integrate the different technologies together.
- [19:33]
B
Yeah, absolutely. I think to your point, you know, I've been in now several meetings recently in the power renewable space and now everyone's trying to ramp up on being gas experts. So it's just really interesting to see, you know, in these conversations.
- [19:47]
A
Yeah, very true. So, Naina, I wanted to go back then to you on this and just to think about, you know, what that means in terms of decision making. Maybe just to tell us a little bit about your experience at Hess then. So this is oil and gas exploration and production company, had an interesting record if people know the history, but founded by Ilhes way back, was originally in the fuel distribution business, diversified, then became a very, very successful upstream player and in particular had this incredible success in Guyana and you know, these massive fields which are very important now to future oil supply. But there was always very strictly an oil and gas company. When you thought about the energy transition and where it was going, how did that affect your decision making? Did you ever, for instance, think about investing in renewables? Was there something or did you think about what it meant for the decisions you had to make in terms of the types of oil and gas assets you invested in? What did it mean to you?
- [20:49]
C
You know, Ed, when we started, it was about 2019, right? And there were a bunch of net zero commitments coming out. There was a wave of net zero commitments coming out. And Greg, Greg Hill, who was our president and John Hess came to my team and basically said, you know, figure out net zero. What does this mean? So we took our time to understand this space because we're really good at our net knitting, which is oil and gas, more oil than gas. And we were really focused on our portfolio and understanding the role of the assets in the portfolio, et cetera. And so when we started looking at net zero, a couple of things came to play, right? And we distributed sort of the solutions in three big pillars. How can we decarbonize our own operations? Are there technologies or additions in the adjacencies of our current assets that makes sense for us to invest in so new technologies like maybe CCS or even hydrogen, if that made sense. And then we knew that at some point there was going to also be a financial instruments angle to it, so offsetting what we can't decarbonize ourselves. And so when we started looking at each of those pillars, our decarbonization angle was the one that we could very systematically go after because it's just basically building out a marginal abatement cost curve with all of your solutions and then going after it from an operational perspective on the CCS side, as well as some of the newer technology side, like renewables as well, we looked at a bunch of different things and we realized very quickly, and this is one of the learnings that we had, is to make a switch. And this probably goes to what you were saying, Jason, about the European majors as well. To make a switch of a business model, you need an investment thesis that makes sense. Right. And we looked at our knitting and we said, can we actually move into some of these spaces? Are we, you know, what gives us the unique advantage to move into some of these spaces? What gives, you know, how are we actually going to create value down the line? How are we going to bring costs down the line? And then what portion of our portfolio can we actually invest in that space? And we very quickly realized that the most sensible solutions in order to decarbonize were solutions that were in adjacencies to our current assets. Because we were really good in the Bakken. And so we started exploring what potential carbon capture in the Bakken would mean. We've got Asia, which is high CO2 assets. And we looked at what does carbon capture mean? Again, carbon capture is similar in, in terms of skill generation, Right. Between oil and gas and ccs. And so again, it's the adjacencies of the assets that we were looking at.
- [23:40]
A
Right. Really interesting. And actually another great example of kind of interconnectedness between sectors that you're in, upstream oil and gas. But you realize you need to know about carbon capture as well, and that's something else you're going to be wanting to explore. So I wanted to talk a bit about data, Jason, because I know there's a big, big theme of yours, and you talk about bad data leads to bad decisions. You talk a little bit about what you mean by that and what are some of the big issues in terms of energy data as you see them?
- [24:08]
B
Yeah, I mean, certainly we can spend an hour on this, but I don't want to put your listeners to sleep, but I think what I would say is that when you look at the topic of data, we're in a point in time now that we have access to an unbelievable amount of data. And we kind of see three of traditional mistakes made with data, but the first is just the lack of using the amount of data. So when you looked at historical ways in which decisions were made, humans just tend to think very linearly. It's cause and effect. Right. And so the amount of data they collect is a very small amount of data and oftentimes misses correlations or interdependencies between data sources because the human eye just doesn't see that. And so when you start looking at these machine based programs, they just crunch massive amounts of data and they start recognizing patterns or dependencies that humans don't see. And so when you look at the amount of data that's out there, Wood MacKenzie is arguably the largest proprietary aggregator of data in the market right now. We have the most proprietary data of anyone out there. And we see this as an arms race. And so we continue to do M and A to buy more and more data sources. And we see the data sources out there. And what you can see now is beyond kind of traditional ways of gathering data. You have everything from drones to, you know, in our case, we have the largest sensor network worldwide where we track energy movements, power lines. We now have sensors now on hyperscaler data centers tracking energy usage. So this is really an incredible amount of data that's out there, whether it be drone, satellite and sensor data that's being put into instrumentation with IoT, et cetera. So that is clearly one kind of thing is the more data the better. But the second thing is sometimes we see an over reliance on synthetic data and that's probably a little bit worth more of exploring is that now every vendor out there or company says that they can use machine learning to estimate data, which I call synthetic data. It's really not actual data. And you find out it's only as good as its source, you know, and you can't just trust that a vendor who says they have modeled this data is actually real data. I mean, I gave the example of sensor data. When you look at congestion, there are obviously ways that you can potentially approximate congestion via modeling. And it's much worse than actually, actually having the true sensor data. Another one that we see @Wood Mackenzie is when you look at valuations, Wood Mackenzie has values on every energy asset in the world, whether they be solar, wind, oil, gas. One of the biggest impacts of valuation on energy assets is really the tax and regulatory treatments from those local authorities. And that's not something you can estimate via a model. It has to be collected through human intelligence or human relationships. So I'd say that's the second kind of big mistake. And the third one really is lack of integration. As we talked about the book Connected, the data is connected and understanding how the data you collect connects, whether it be power, our data has to also understand what the impact of that is around EV or oil or gas. And that's another kind of, I think, fallacy or mistake that's being collected is the data still is being collected, very siloed.
- [27:24]
A
That's very interesting. Yeah. So, Sanayna, how do you think about this then? So you've been a user of data. You're a user. You were a user in one way running strategy for Hess. You're going to be using data, presumably in. In different ways, but not completely different ways. In your new role at Bernstein. What do you want from data? And what are the kind of the big problems that you encounter when trying.
- [27:49]
C
To use data from a strategy perspective from within these energy companies? Right. Strategic planning in energy is actually all about data. Right. And the scale and complexity is so interesting to understand it because you're not just. We weren't just forecasting oil demand. We were linking it to associated gas. If there is associated gas. What does that do to gas prices in the U.S. what does that mean for, you know, power markets? If we're looking at things like VPPAs or, you know, to decarbonize just on the oil side of things, there's also EV adoption, which means critical minerals and. And even forecasting physical climate risks. Right. And so for us, data can absolutely help in so many different ways. I think AI can help in two ways for strategic planning. It's what Jason said. It can process vast interconnected amounts of data sets really much faster than any human can because we don't have the mental ability to keep so many nodes of uncertainty. If you imagine a decision tree, you can also respond to policy changes really quickly. So now if you have. We use scenario analysis, for instance, if you have future scenarios that you're looking at, you can update that scenario analysis really quickly. You're not waiting for this insane Excel model that takes six minutes to open and that two people in a room can sit and do. Right. You can have basically a reservoir engineer in Guyana, a data scientist in Houston and a strategist in London all looking at the same scenario analysis. And that's really useful. I think that's the second piece, which is it breaks down some of the siloed decision making. I think the biggest piece that we saw with the benefit of using extended data sets is bias recognition. Right. Because if you're using a small data set, you're essentially propagating that bias into your decision making. And so bias recognition was an interesting way to think about why datasets can help not using too optimistic type curves can really help with not over, you know, over promising Production data. Right. And what it can do with unstructured data is really helpful as well. So whether it's monitoring flaring data from various sources, there's a bunch you can do there. I think the only thing I'll say is the key point is AI can be very useful and data can be extremely useful. It doesn't replace judgment though, Right. So it augments it. And so it helps to elevate everyone in the organization to do more with the data. And in this, I think you guys saw the article in the Journal today. You know, another super major is, is letting people go. So if we're going to be in this new world of using less people to do more and keep the same level of production and bring costs down, companies that are going to be differentiated are the ones that are really making use of the data set correctly.
- [30:56]
B
Sunayana and I had a chance to meet almost a year ago when I first started. And I really think Sunayana had one of the most advanced teams that we saw of any of the large majors that we met with. And obviously it's a credit to her and her team. And I think obviously when you start looking at smaller producers out there or even a small developer in the PNR world, they don't have access to that team of, of folks. And so obviously it becomes how do they actually leverage the power of AI to make better decisions and better planning? And maybe just to piggyback off, further off of what Sunada shared, when we look at AI, an entire chapter we wrote in a book around this, but we kind of identified kind of four major changes of AI. And Sunana kind of identified one of them, which was the enhanced compute power allows you to run scenarios. Now you're talking instead of of dozens of scenarios, hundreds of the scenarios, millions of scenarios, and allows you to run them in real time. So in essence, instead of taking weeks or months to make changes in outlooks, you can now do it in days and if not hours, and hopefully someday real time. I think we found this very important, particularly when we were meeting with clients around the tariff changes that were going on. They were changing obviously very rapidly. And how did that affect the planning process? But that's definitely one of them. The other one that we felt very strongly about is having an integrated outlook. You know, it's not just one model. You know, Wood McKinsey has 1100 models. You are going to need different type of models for oil and gas or the local markets and power. Even within ERCOT or, you know, jpm, they had different Behavior characteristics that may require different models. The question is then how do you integrate this model into an ensemble, into an orchestra? You know, if you have all these different models which are instruments, how do you have that orchestrator that pulls it all together? And that is obviously where I think AI starts playing with large neural networks. The other two areas just to quickly mention is also prop based. AI is a game changer. We have an entire chapter based on this. I think it was affectionately called the Tyranny of the Propeller heads is that often, too often the modeling is actually done by one person in a corner office or in a corner room or corner cubicle. And now with prompt based AI, you can actually have executives engage with that model real time asking very simple questions like what is the EV demand going to be if the price of oil drops to this? You don't have to ask a modeler to then run the scenarios. And three days later it appears it's all done real time. And the last thing I want to maybe spend a second on, which is a pretty nerdy topic, is hyper modeling. So I won't go too much in depth and our CTO could give a course on this. But the concept is historical. Historically, modeling was done linearly via Excel spreadsheets. What increasingly you find is that the world is much more probabilistic. And as the world is problemistic, it opens up now all sorts of different models and there's an infinite number of models you can use. Tree models, reoccurring neural network models, TFT models. What you find is that you're probably going to end up using different models for different use cases or scenarios. But what hyper modeling allows you to do is now you can use AI to test these models real time and run scenarios where you can actually use these AI to actually find the best model for you that best predicts the outcome the best. And then you can actually use an AI suggested model to then be one of those instruments in the orchestra which you then pull together into a full sample. And that's a game changer. Historically you would have to test each model individually manually. And now you can run these compute, massive compute over millions of scenarios and then find out which model best represents that particular scenario and then pull it into a final orchestra. So as I said, it's really fascinating what's going on and it's happening real time and really game changing.
- [34:53]
A
Yeah, that is really cool. I have to admit that was a new word to me. Hyper modeling is not a concept I've come across before, so thanks for that. It's been educational and as you say, very, very exciting potential. So where are we with that then? Is this something we are using now, something we'll be using in the near future? Where's it got to, Ed?
- [35:10]
B
Absolutely. AI is a substantial strategic investment for Wood mackenzie. When I first started, we kicked off this initiative called Synoptic, which is really an umbrella term for all of our AI initiatives. We have multiple dozen now initiatives where we're embedding AI in our products to provide better insights and better predictive capabilities. Also, we've been adding some incredible capable people. Our CTO, Bernardo, incredible PhD in computer science and also chief architects and literally best and brightest in machine learning. And all of this is really to help drive better tools and insights which will make our human researchers even more capable and providing better insights to our clients.
- [35:54]
A
I think we're just about out of time. We should be wrapping it up soon, but just before we do then. So, Jason, look, if people have been been listening to this and people have been listening to you and find it convincing, what should they do and what is your kind of advice to people then? If you are a decision maker in the energy industry, let's say, how should you be responding to this changing world?
- [36:15]
B
Yeah, you know, first and foremost, I would just encourage them to go to www.woodmac.com and download the book. And it's a light read, it's about 90 minutes and I think you're going to really enjoy it. It's a lot of really big, heady ideas that I think are a bit provocative and will definitely, I think, get you to think a bit differently. But outside of reading the book, the way we typically think about things is kind of we call the winning trifecta, which first and foremost is what Sunana and I just talked about, which is people like, you have to have the best people. This is not simple stuff. And finding the foremost experts is key. I think the second one, obviously we talked a lot about data and using big data to help make better decision making. And the third one is leveraging tools like AI to help in that decision making. And the last kind of, you know, part of the trifecta, which really kind of the big thing that captures everything is that's gotta be integrated. We would maintain, and I give this analogy whenever I meet with the customer is the way you engage with your doctor is, and you try to get medical advice is you could try to go to specialists on everything or you could use a family doctor or use, you know, a concierge doctor, someone that can provide you a holistic view on things, can recommend specialists when necessary, but provide you that holistic, integrated perspective. And we would recommend that's how you approach the energy world. Right now. The world is too siloed. There's too much of a reliance on specialists. And I would advocate this is not just at a corporate level. I'd also argue this is also at a government level, kind of an over reliance on a lot of individual industry folks that have certain kind of policies they're trying to advocate for for. So we think an integrated holistic partner that can help you interpret and bring a holistic view is directionally a huge part of the advice that we'd recommend going forward.
- [38:01]
A
Yeah, that's a fantastic point. And as you say, it's very possible to see, I think that kind of truly comprehensive understanding to kind of see all the different parts, see how they fit together is sorely lacking in a lot of places. And definitely kind of people could benefit from seeing that picture more clearly. Solana, final thought to you just in terms of that aspect or just in general in terms of what you think energy companies, what energy decision makers are going to need to do to be successful in this changing world, what do you think? What's it going to take to deliver the results that people are going to want to see?
- [38:42]
C
Sure. I think I'll just double click on something that Jason said and I think one of the things we talked about, scenario planning, think I scenario analysis a little bit. I'll just probably, you know, talk about that a little bit more. And I think one of the things that's going to really help companies is understanding the role of the assets. Right. Because the benefit of scenario planning is not about predicting the future. It's looking at a plausible set of futures. Right. And if we do that at the asset level and you understand the role of each asset in the portfolio, right. So scenario analysis on the asset level, and then you take it up and you do the scenario analysis on the portfolio level to see how the entire portfolio behaves collectively. Then the real value comes from sort of asking, you know, across scenarios, what adjustments are you going to make on the portfolio level to achieve the best possible outcome. Right. Because I think one of the things that energy companies, you know, are challenged with, and Ed, you and I talked about this offline a little bit, is this short termism, right? You're trying to basically, you know, end up trying to meet every quarter, targets every quarter, but you're also trying to predict, you know, what life is going to be 1015 years from now. So short, medium term, and so the understanding the role of the assets and then how they play towards the portfolio, that's really your strategy. And then the quarters just become a piece of communication. Right. You communicate what your strategy is and any missteps that happen, you communicate why those missteps happen, and you explain the role of each of the assets as part of the communication on the quarter. Right. And I think that exercise, along with all of the data and the hyper modeling. We never did hyper modeling. It has, but all of the hyper modeling that Jason talked about, it really allows you to say, are there any clustered risks at the portfolio level? Are there any options that exist at a combined level? So the power of scenario analysis just really gets enhanced with the data and AI tools that we now have. The companies that thrive are probably the ones that are going to embrace some of this uncertainty. It's what Jason said. Everyone talks about uncertainty, but it's how do you actually model out plausible futures so that when things change, you can quickly adapt? Right. It's hydrocarbons for resilience, but also low carbon technologies for growth and then using AI for quickly moving towards a new future. And I think winners are going to be folks who manage across all three of those pieces.
- [41:31]
A
Jason, did you want to say anything else final for the.
- [41:34]
B
Oh, you know, I think Sunana had, you know, captured a lot of the sentiment, you know, and kind of to your point, you know, Samantha, about ultimately, you know, kind of, you know, becoming successful in uncertain world, part of it is getting better at predicting, but part of it is also getting more agile. Right. So in this world of a lot of volatility and change, being able to be very agile in not just decision making, but operational is going to be key to future success.
- [41:59]
A
All right, thanks. And final. Any final thoughts? Anything else we haven't talked about that you wanted to raise?
- [42:05]
B
Yeah, I mean, I think we obviously have talked about the need for better predictability and better scenario planning and better agility. But I do think there's a positive. There's a half glass, half full opportunity, which is there will be well over $75 trillion invested in this energy evolution over the next several decades. And that presents massive opportunity for investment growth, increased irr for those that actually get it right. So I think if we embrace many of the topics that were discussed in this, this podcast and in the book, I think it really creates a massive opportunity for wealth creation for companies, individuals and nations.
- [42:43]
A
Yeah. As you say, an enormous amount to do.
- [42:45]
C
And I'll add to that by just saying, I think this is actually the best time for this sector because some of the capital that went in was pretty inefficient over the last five to 10 years. And it came from various private sources, so philanthropy, family offices, et cetera. And now it's starting to move to public. And I think there's a better understanding of the economics as well, without the subsidies. So it's a great time. And I agree on that completely.
- [43:15]
A
Absolutely. Yes. I remember when I first started getting into energy, whenever it was sort of 20 years or so ago, I found it an incredibly exciting industry then. I think it's easy. Even more exciting today, as you say, for a lot of the reasons you've been talking about. So unfortunately, we do have to leave it there. But it's been great talking to you. Many thanks, Jason.
- [43:35]
B
Well, thank you very much, Ed. Thank you, Sanana.
- [43:37]
A
Yeah, many thanks, Sanayana.
- [43:38]
C
Yeah, yeah, thanks. And thanks, Jason. Congrats on your book.
- [43:43]
A
Yep, absolutely.
- [43:43]
B
Congrats on the new role as well.
- [43:45]
C
Thank you.
- [43:45]
B
All right, thank you.
- [43:46]
A
Hope to talk to both of you again soon. Thanks very much to our producer, Dan Cottrell and a bottle of many thanks to all of you for listening. We really do value your feedback, so please do keep that coming. And we'll be back soon with all the latest news and views on the future of energy. Until then, goodbye.