Bent Flyvbjerg, Author Of How Big Things Get Done Talks Pumped Hydro, Transit & More – CleanTechnica

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Before How Big Things Get Done became the best-selling business book of 2023, before it topped best of 2023 lists and before Bent Flyvbjerg’s interview schedule became jammed with every media outlet in the world, I had the opportunity to sit down with him for 90 minutes in early 2023 when I was co-hosting CleanTech Talk. Here’s the second half of that discussion with a full, lightly edited transcript and embedded podcast.

Why did I managed to get this opportunity? Because Flyvbjerg and his co-author Dan Gardner had stumbled across my ongoing assessment of the natural experiment of nuclear vs renewables in China, where the modularity, global supply chains, and parallizability of construction have shown clearly that renewables scale a great deal faster than nuclear. They saw that it worked incredibly well in Chapter 9, “What’s Your Lego,” and asked if I would permit them to reproduce it. I said yes, of course, and so I have a tiny place in an amazing and incredibly useful book.

If you haven’t read Big Things, get a copy now and put it at the top of your reading list.

Michael Barnard (MB): Hi, welcome back to Redefining Energy — Tech. I’m your host Michael Barnard. This is a repeat of a discussion I had 18 months ago with Professor Bent Flyvbjerg, author of the 2023 best selling book How Big Things Get Done. Listen in as we talk about pumped hydro versus batteries, small modular reactors versus nuclear reactors, and much more.

And that’s the next topic, which is pumped hydro storage versus batteries, because there’s a really interesting one, pumped hydro storage. They’re billion dollar plus projects. You have to tunnel a ten meter diameter penstock up to 8 km through solid rock and you have to build a reservoir 400 meters or higher below the lower reservoir. And you have to put in four to twelve pumps that are regenerative pumps. And building that tunnel 30 meters, 10 meters in diameter through kilometers of rock is non trivial.

That’s the megaproject part of pumped hydro. Everything else is components and trivial stuff. We know how to build reservoirs. We can buy pumps off the shelf, we can buy the electronic and control systems off the shelf. That’s thing one thing two though is batteries. Batteries are absurdly modular. Like Tesla and Voxela and all these other companies are now delivering containers full of cell based batteries. Thousands or hundreds of thousands of small tiny modular components plugged into a repeatable pattern in a deliverable container framework that’s going off the shelf. This is an interesting aspect, but I always like to tell people this is that introductory piece. And I have a big question for you at the end of this. Pumped hydro is a. I would say it is modular in the sense that it uses multiple turbines and knows how to do that.

It’s highly commoditized hardware. There’s not a lot of innovation in pumped hydro. Second, it’s by far the largest. This is stuff people don’t know. It’s by far the largest energy and power grid storage in operation globally today. Like orders of magnitude higher than anything else. And thats because we built a lot to give coal and nuclear plants something to do at night to justify those investments in gigawatt scale generation. The last point is its also by far. This is another weird unknown to me in the power and energy world. Its also by far the largest power and energy form of grid storage under construction today. We hear about all these batteries going in, but the batteries are going in the 20 to 40 to 100.

Look at Mark Wilson across the water from you in Scotland, Mark Wilson of intelligent Land Investments has three pumped hydro facilities that he’s developed, and he’s currently in the process of selling to people who will construct them. He’s done all the transmission interlocks and got all that stuff done. But turkeys nest on top of a hill next to Loch Ness, and Loch Ness is a lower reservoir, and those three have 2.5 gigawatts of power capacity and 60 gigawatt hours of storage, which dwarfs all the battery projects in Europe. Thats one small developer in one small country. Its an interesting question. Where does the modularity and that repeatability in that fat tail stuff have? And do you have pumped hydro projects in your data set yet?

Bent Flyvbjerg (BF): Not as a separate category. We have dams, as I mentioned, as a category, but we haven’t singled out pumped hydro yet. But we’d be very interested. Actually, very often the way new projects get into the data set is that somebody from the outside contacts us and say, we would like to work on this. Will you help us? Explain to us how you build a good data set? And then we help them, and we build a good data set for whatever it is, what asset they’re interested in this case, pumped hydro. And I’d be very interested in working on that, but we don’t have the data yet. My advice, though, would be to people doing pumped hydro, and they probably already know this and don’t need my advice, but it would be.

And, you know, a lot of organizations are contacting us now, trying to help them with the question, how do we modernize what we are doing further? Even if what they’re doing is very modularized already, how do we modularize further? Yeah, and that’s the key question. And that’s what I would say. If you’re doing pumped hydro, that’s what you need to focus on. The thing that immediately gets my attention is like, digging is involved, and we know anything that involves digging is really risky. You know, when you start digging, you don’t know what you find. And a lot of the fact chains actually in construction comes from digging and not knowing what you’re going to hit on the ground. So that’s something that needs to be thought through carefully.

For pumped hydro, how do we avoid the risks, the unique risks that are involved in digging?

MB: Yeah, I’ve spent a lot of time randomly looking at tunnel boring machines and the multiple failures over the years as they run into much harder chunks of rock, igneous intrusions from below, and fault lines and water and stuff. You document a few of those and thats kind of an interesting thing because pumped hydro is an interesting question. I encourage you to think about it simply because it is so big and it gets a little pressed. Its one of those hiding under the surface of the battery hype. Batteries are amazing and I love them. I recommend I published projections on global storage through 2060 and theyre strongly present. But I think pump tiger is going to win. Except theres a nagging question for me about the modularity perspective.

BF: Well, it doesn’t matter if it’s working. It’s going to win, especially given the fact that it can scale to the level that you’re talking about there. I consider pumped hydro as another battery. It is a battery in a sense.

MB: It gets rid of a lot of the fat tail risks. The Australian National University study from five, six years ago by Matt Stocks, very interesting. What he did is I was looking at machine learning and clean tech solutions globally. That one popped up on my radar and I talked to Matt stocks and what hed done was hed actually not used machine learning at all, which was interesting. Hed just taken a GIS dataset and queried a bunch of questions. And the questions were, two sites within this much horizontal distance, kilometers, a couple of kilometers horizontal distance, at least 400 meters of vertical distance, because its MGH, you want higher for more volume, more mass to get better storage. And then he said it has to be near transmission, it has to be off protected lands.

So getting rid of two or three long tail risks and then projected there’s 100 times the resource of the worst case scenario for storage globally. So it’s an intra, yeah, I know, it’s just a huge resource. And what that means is if you have a 500 megawatt meter head height difference and a gigalitre of water, that’s a gigawatt hour of storage. Yeah, it’s just a huge volume and it’s just water is a cheap commodity. I’m beginning to question for myself, because I’ve got this cognitive dissonance between the modularity stuff, which I deeply internalize, and my preference from Taijur. And the question is, can I justify it by saying water is the ultimate modular resource? That’s pretty tough.

BF: Yeah. Anyway, but what I think is that this is good news, and this is one of the things that struck me when we wrote the book is that, like I mentioned earlier, we saw, wow, it actually turns out that solar and wind are thin tailed and therefore easy to deliver. How lucky is that, given the problems we have with the climate crisis? And now you’re saying the same for pumped hydro. So those are really good news that there actually are things there that can really be scaled, and it’s exactly what we need. Right. So I’m actually quite optimistic about being able to solve the problems with the climate crisis, given the technologies we have. If we can get our stuff together to scale it fast enough.

That’s really what it’s about, that we need to be able to do this at a scale and at a speed that is unprecedented.

MB: And I’d like to lean into this now because you talked about transmission and you also talked about two examples of high speed rail. And I look at high speed rail and I say that should be like transmission. It’s a linear project. It has repeatables pieces along the way. You have to prepare a foundation, but it’s just a repetitive process of putting in place. So why does high speed rail fail in your examples and in your data set? And why doesn’t it? And one of the questions I have to ask you is, does your dataset include the 40,000 high speed rail that China’s built?

BF: No, it does include high speed rail, but not the 40,000 km China has built. Not because we didn’t want it. We actually asked for it. But, you know, it’s difficult to get data in China. That’s why were so happy about your data that you were actually able to get them. We did get through the backdoor and got data for chinese transport infrastructure projects through the World bank. The World bank is working in China, and they have certain requirements regarding getting data on the projects that they fund, which meant that in DC they had data on some projects, like just under 100 projects in China, but not from the high speed rail network. This is conventional raid rail and road projects that we got data for. So we don’t have the data for China high speed rail.

We do have data for quite a lot of high speed rail around the world, and it’s not performing well. It’s not as bad as nuclear or the Olympics. It’s sort of in between, sort of midway in between. It’s also not as bad as it projects or defense projects. Defense projects are also terrible. Aerospace projects, it’s somewhere in between. And I would say it’s because, again, of the digging. There’s a lot of digging involved in building a high speed rail line, much more than in transmission is easy. Transmission is basically following the surface of the earth. Or if there’s digging involves, that would because of landscape reasons. So in the city, you put it underground, but it’s not like a bored tunnel, which is serious digging. So it’s much more limited digging.

And if there is any digging at all with transmission, whereas with the high speed rail, it’s very serious digging. It’s tunnel boring and also bridges. So there’s a lot of bridges and a lot of tunnels on any high speed rail line. And they are the two things that will create problems. We have enough stories, and you know the story about the high speed rail line in China where the trains derailed and quite a substantial number of people were killed and top civil servants were jumping out windows and got one person got a death sentence and so on. So it’s not like high speed rail. It’s been non problematic in China with just the evidence that we have.

And based on the evidence that I have from conventional rail in China, I would be surprised if high speed rail in China was a walk in the park. I don’t think that’s the case, but we’d have to get the data in order to give a final verdict on that.

MB: Yeah, I think that they did well, China being very engineering and stem centric and very economically focused. I would say they cut off some of the fat tail simply because they, unlike Europe, where high speed rail runs on different gauges in every country practically, and in the UK, where I believe high speed rail runs on active different gauges and regular rail, et cetera, et cetera, China standardized a bunch of that stuff. And if you look at some of their big high speed rail stations, like in Beijing, every train is the same as every other train. You talk about that repetitive repetition. If we look at, you have an example in the book, which another subject I’m fascinated by, which is subways in urban centers. Same problem, North America and the western world. The subways typically are really expensive. And Alain Bertaud actually does.

I feel like Alain Bertaud must be an honorary urban geographer. Do you know Alain Bertaud and his work?

BF: Alain? I know the name, but I might have come across the work. There’s so many people and so much work.

MB: So many great people.

BF: Exactly.

MB: Alain Bertaud was the chief urban planner for the World bank for like 50 years. And so he went into China when it was going through the economic transformation to assist them with urban planning. That made more sense. And was there he went into the Soviet Union or to Russia after the collapse of the Berlin Wall to assist them to try and figure out what to do with the stalinist apartment blocks and city development. And he went into African countries with no cities and laid out road grids. And his perspective on subways was interesting. He said, the subways in New York are just as the price per square meter for land is the same as the land directly above them. So it was kind of an interesting urban geography point.

But you also counter with a specific example of an urban subway problem project which came in cheap, I think, 76 stations. Can you tell us about that one?

BF: Yes, that’s Madrid. But before we go to Madrid, let us just return to China to round off our discussion about the high speed rail in China. I would say that I would be very surprised, given the volume of high speed rail that has been built or the length, rather, of a high speed rail that has been built in China. If there wasn’t some positive learning, I would be very surprised if went iN, started this, and found there’s no positive learning. That would be almost impossible building that much in such a short period of time. So I would expect there to be some of that, and that would support your argument. But the bottom line is, Mike, we don’t know because we don’t have the data from China, and this is too often a problem when we work with China. China is so important.

As expert on mega projects, I can tell you there’s no other country that is more important because there’s no other country that is building more megaprojects than China is doing and have been doing for decades. So I would just love to get my hands on those data. I’ve been in China, I’ve talked to my chinese colleagues about that, and they say, forget it. You know, that it’s just not the way China works. If the central leadership says that this is what the data show, that’s what the data show, and they don’t want any research that’s looking at what the data actually show. If there’s an official story about what the data show. So that’s the problem. Work with the projects in China and many other things in China. This is not just for megaprojects.

Economists who are starting the national economy have the same problem. Okay, that’s China. Let’s go to Spain now and to Madrid, because that’s one of the places where we found a team that were totally able to beat the odds in the casino. So the odds for urban rail are not that good. You will have large cost overruns on average, in constant prices. You would have like 40, 45% cost overrun on average. You will have delays, you will have lots of passengers in the forecast that never show up in reality. So that’s your standard urban rail project on average. So urban rail actually follow the iron law over budgets, over time, on the benefits, over and over again. Except we found this example in Madrid.

An outlier that were built twice as fast as urban rail is normally done to schedule at half the cost to budget and basically got the passengers that they projected. Yeah. Now, how on earth did they do that, you know? And they did it by doing what nobody thought could be done, modularizing on the ground rail. Like modularizing subway. So this team in Madrid, they figured out there’s got to be an ideal length for a tunnel boring machine. Boring a tunnel for a subway. So they started measuring that. What’s the ideal length for one tunnel boring machine? With one team running that tunnel boring machine, they figured it out. And then they were in the process of doing the largest expansion of a subway system in the world ever at the time. Now China has done more, but. But this was outside China.

Madrid’s expansion was much larger than Yuushin, and they figured, we gotta get this done. This is the policy with Madrid. We need to get it done. And then they just hired as many tunnel boring machines as they needed for whatever length it was that they were building. And they would actually get eight tunnel boring machines and teams in the under Madrid to work at one time when they had the most teams going. And instead of taking 8, 10 years of building an extension, they would take four years to build the extension. They would work around the clock, which is actually not common, usually for different reasons. Not to disturb, not to have construction going on at night and on weekends, there’s a lot of downtime.

And they negotiated with the local community groups that, hey, we can take ten years to do this or we can do it in four years. If you work around the clock, 24/7 we can do it in four years. If we abide by the usual rules of not working at night and not working on weekends, it will take more than twice as long. What do you prefer? And they didn’t hide that. They preferred to do it twenty four seven. And they got that through. The community groups actually accepted that. So that’s one thing. Very good collaboration with local community groups. And then this thing about modularizing the different parts of the metro and also stations in many metros around the world, in Danden, in Moscow, and so on, you will find that the stations are almost like pieces of art, and each station is different.

And you get fancy architects to design the stations. In Madrid, they decided, no way, we’re not going to invite signature architects. So signature architecture is one of the other areas that typically has very large cost overruns and delays and so on. And they said in Madrid, why would we be so stupid that we would invite that kind of economic risk in by having specially designed stations by famous architects? We’ll do the exact opposite. We’ll make a very nice, big, airy station. We’re not going to drill it. We’re going to do cut and cover. So we just take a big hole and we put in the station and we cover it and that’s it. And we’ll do the same station pretty much around Madrid so that we get positive learning curves. Every time we do a station.

We do it better next time and better after that and so on. As opposed to, if you do bespoke stations that are each designed by what, a famous architect, each one will be different and you won’t be able to get these positive learning curves. So they really maxed out on all these things. They also decided no lawsuits. And, you know, in construction, lawsuits are so common. This is actually, you write the wrong contracts upfront, contracts that actually encourage conflicts. You know, that people start thinking about, how can we sue each other when things go wrong. From day one, this is the first thing, but even before day one, this is what they think about when they design the contracts.

And if you design your contracts like that, when things go wrong, and they always do, there’s always something that goes wrong on projects of the size that we’re talking about here. Then people start suing each other in Madrid, they decided, we are not going to write our contracts like that. We are actually going, we, the client, are going to take on a lot of the construction risk. We are not going to try to allocate this to the contractors, because we’ve tried that and it usually doesn’t work. Even if we thought we had signed it over to the contractor, it always somehow mysteriously ends back with us. So why don’t we just face that fact and then accept that’s the way it is?

And then we get a partnership with our contractors, where we collaborate on getting as few as those risks to materialize as possible, and we pay the contractors to avoid it instead of suing each other when things have gone wrong. So there was like a handful of basic things like that they did in Madrid that worked out. When you put them all together, it worked out beautifully, delivering at half the price, twice as fast, and very functional. Subway. If you’ve been there and you’ve tried it, you’ll know that this is actually a system that really works. It’s very large for a city of the size of Madrid. They have a fantastic metro system.

MB: I want to dig into thinking slow and acting fast, because when you talk about. the duration of megaprojects, my supposition, I don’t think it was crisply laid out in the book, because I think you assume that it’s just so internal to you. But I think when you talk about acting fast, you’re talking about the delivery phase, where construction after the shovel hits the ground until completion. So the duration you’re using is for that portion. So for Madrid, it’s when the first construction site had the first shovel in the ground is the start. But that thinking slow process is intentionally and rightly excluded from it. Now, can you characterize thinking slow versus acting fast because it’s such a fundamental premise in your book?

BF: Yeah, that’s a key premise. One of the chapters is called that. And if we take the Madrid example again, then the thinking slow. Is the leadership in Madrid actually thinking up all these rules of thumb that I just mentioned? We have to have good stakeholder management with the community groups. We’re not going to sue anybody. We’re going to modularize tunnels into segments of optimal length. In relation to what it’s very relevant to, actually, what you talk about, Mike. It’s about the basics. What are the basic physics of this? And getting back to the basics. So what’s the optimal links that one tunnel machine will do? And then we’ll just hire as many tunnel boring machines that we need to do, the total links that we need to do. Right, and the same with the station. So that’s the thinking slow.

Thinking all this through before you do anything, instead of what usually happens, is that people only figure out these things, you know, while they’re delivering, you know, while construction is going on in Madrid, they did it before, and that’s what we find. This is what intelligence master builders are doing. And that’s what we call the people who do it this way. We call it masterpiece. That they’re really mastering what they’re doing, and they are mastering it by what masters always do, is like they really think things out. But then once they get going, they know the clock is ticking. And this is the reason that it’s so important to act fast. Once you’ve got the shovel in the ground, as you say, and for an IT project, of course, it won’t be a shovel, it’ll be something different.

But once you start delivering, you need to go fast. Because that’s how you reduce your risk. We call it the Window of Doom in the book. So there’s a window, and that’s the time window from you start delivering till you finish delivery. That’s the window. And that’s a window of doom in the sense that’s where you can really get hurt and your project can really become expensive, is when you. So if your tunnel boring machine is flooded, like what happened on the high speed rail project in Hong Kong that you talked about, and on a tunnel in Denmark that we also talk about, it’s actually surprisingly common that you have tunnel boring machines that get flooded. And these machines are expensive, very difficult repair because they’re in a hole underground. So they create huge delay if you get problems like this.

But that’s the kind of thing, and that’s why we call it the window of doom. All these things can happen in that window. Obviously, you want that window to be as small as possible. You particularly want it to be so small that no fat black swan can fly through it and mess up your project. And the smaller you make it, the less risk you have of these things, of any type of risk, including black swan risk. So that’s the reason why projects that are able to move fast in delivery have much smaller risk than products that take all the time. A lot of people don’t think of it like, yeah, we have lots of time. It’s not a problem that we take ten years to deliver a project.

Well, let me tell you, it is a problem if you take 10, 15 years to deliver a mega project, which is not uncommon. You can count on that something really bad is going to happen during that period just because that’s the nature of things. That’s history. You’ll have a major financial crisis. You’ll even have a pandemic. As we’ve seen now, nobody has been thinking about pandemics for 80, 90 years because we haven’t had one for about 100 years, right?

MB: A really bad one that our public health surveillance system. And as you may have noticed, I did actually help build the world’s most sophisticated outbreak in communicable disease management system in the world after SARS. And since SARS, we’ve had H1N1, we’ve had ebola, and now this. What I articulate is we have this amazing resilience built in because we’ve mostly learned their lessons, but we keep forgetting because climate change and pandemics are gray rhinos. They’re not black swans. They are expected. But I will say, let’s just take the duration. The median duration in recent decades is it takes ten years to construct a nuclear power plant. And so much stuff is happening so quickly. A decade ago, it was possible to look at the data and say, we don’t know if wind and solar will be viable.

We don’t have good data on grid integration, we don’t have good data on how they’ll integrate with markets. We don’t have good data on grid reliability with significant portions of that. And they’re still fairly expensive. But any nuclear reactor started a decade ago in construction that is coming into market today, it’s facing a radically different market competitive situation because wind and solar have proven themselves grid reliable, cheap, stable, and are now starting to take over ancillary services on grids as well, which is really interesting, but really nerdy and ill put that aside. The point is thats that window of doom. The more you can shrink that, the more likely that your business case assumptions for something are still going to be valid when it goes into production. And things like the Ukraine war, for example, don’t impact what’s going on.

BF: Yeah, you know, Phil Tetlock, who is co author with Dan, my co author on the new book, on the book on Superforecasting. So Dan wrote Phil Tetrock called Superforecasting before he wrote this book with me called How Big Things Get Done. And he has a law that I call Tetlock’s Law. And that is, you know, you have a certain reliability of your forecast the first two or three years of the forecast. And after three to five years, you can pretty much forget any certainty at all of your forecast. So that tells you everything. That means that you actually need to have a substantial part. Maybe the major part of your project needs to be done within two to three years.

You should have as little as possible beyond three to five years because that’s the completely certain part, and that’s where the window of doom will bite you. So that’s why. Yeah, that window needs to be kept real small and we can see it in the data. It’s very clear. This is not something, this is not speculation, this is something that we can see it in the data, that the faster you are, the lower the risk you get. And that’s you actually win on two fronts. You reduce risk generally, and even more dramatically, you reduce black swan risk.

MB: Well, there’s another thing that I’d like to call out that you articulate clearly about the Tesla gigafactory and about wind and solar farms, which is that modularity enables you to start accruing benefits before you’ve completed everything. So that ones the point where when a wind farm is in, 10% of it is in, it can be generating electricity, while another 90% of the wind farm is completed. Similar for solar farms. Do you want to speak more to that? I feel that is underestimated as a shrinking of the window of doom.

BF: I think that the whole discussion of benefits is hugely under discussed and underestimated. They are much more important than we think that to get to the benefits is really important. And the reason that became so clear with Tesla was that at that time, Musk was not the rich guy that he is now. He was actually in huge debts. And so when he was building his first gigafactory, which was called Gigafactory One at the time, and it’s now called Giga Nevada, because it’s the gigafactory in Nevada. And heard that it would take five years for the normal construction industry to build a factory like that. He said, no way. I mean, if I have to wait for my revenue stream for five years, I’m dead. Tesla is not going to exist if I have to wait for five years to get to my revenue stream.

And he said no. And he didn’t talk about, as far as I know, talk about Tetlock’s law, but he acted as if he understood this law. We need to be in business and generate revenues within the first year, not the first two or three years, the first year. So he designed the factory. He said, like, let’s not even talk to these guys in conventional instruction and know this for a fact, because people from. I know people from conventional construction who tried to call Musk and get a dialogue with him about building the first gigafactory, and he would not talk to them. He said, we’re going to reinvent this ourselves. Not a lot of people know this, but this is actually the secret sauce of a lot of what Musk is doing, is that he rethinks things to the basics.

And modularity is actually, or standardization is a key to that, both for Tesla, but also for SpaceX and others. You can see it if you start looking at what he’s doing and including the gigafactory. So he actually decided on a design where the gigafactory was consist of 21 modules, where each module could function as a factory in its own right. And then, so you just build one of the 21 modules, and they did that within the first year they actually built several within the first year and they were immediately in business. They were producing batteries. And the famous, what is now called the Powerwall was coming out of there within one year. And they had a revenue stream that plow back into Tesla and finance their growth.

And then they would build another module and that would be combined with the first, and now they would have a larger piece of factory and so on and so forth. And that’s how scaled up the factory. And they also had positive learning to the degree that they realized we actually don’t need as many modules as we thought we did, because we’re getting more and more efficient the more we do this. So we can now produce more volume of batteries in fewer modules of factory. So they got these kinds of efficiencies through the positive learning curves. So that’s a clear story about how modularity can work positively for you. And, yeah, we included that in the book.

MB: Well, and for my primary concern, which is electrical generation, though I dealt all with transportation as well. For the question of wind, solar and nuclear, as soon as you’ve got the transmission link in and you’re putting in your first wind turbines or solar panels, you can actually start feeding electricity to the market. But with nuclear, you have to be all the way to the end and.

BF: Have to go through a whole bunch.

MB: Of regulatory approval and you turn it on in one day. The gigawatt gets turned on in one day at ten years out and all the debt and revenue has foregone. Its just that problem until then. Whereas with the modularity solution, thats part of the reason its so advantageous. I think the takeaway for institutional investors, policymakers, energy strategists is they should look at that chart in your book. I’m not going to say what page it is for the simple reason that I read it on Kindle. And page numbers are wonky on Kindle, but its. Which chapter is that? Amazing chart of variants.

BF: Actually I don’t have the final book yet. Can you believe it? It just came off the press. So I don’t even know if the page numbers are going to be the same, but I think they are. So let me just find that chart for you. So that’s the chart with the variance. It’s on page 173 and it’s the final chapter called What’s Your Lego? That’s chapter nine. So chapter nine is called What’s Your Lego? Page 173. There’s a diagram with a variance on different projects.

MB: Yeah, I think this is such an important part of this. And as you say, this is the first time you’ve published it. I think every policymaker, strategist, institutional investor should buy this book or that chart in that chapter, and then they should look at that and say, what is my risk profile in my portfolio of major infrastructure projects based on this? And what can I do about it besides call up Ben BF and his firm to help me figure out how to modularize this and avoid stuff? Because you’re a small. As a buddy of mine says, you’ve created frameworks and you help sell people ladders. You help them with their problem, but there’s only so many ladders you can help people with.

BF: Yeah, we do talk to institutional investors from time to time, including pension funds. So, pension funds in Denmark, my home country, are big investing infrastructure, including energy infrastructure, wind farms and so on, and they’re beginning to get it. But it’s actually taken a while that the thinking in the financial sector is unfortunately so conventional. And they have all had the same statistics, 101 courses, that they don’t understand, extreme value theory. This is what Nassim Taleb has been pointing out all the time. He’s really been pounding this message that this is the problem. And I can say that my experience confirms that it is a problem. But I do think that we’re beginning to get a hold through, and we are trying to explain that the risks that you’re looking at are not the relevant risks.

It’s completely different risks, and those are the risks that we try to highlight with this diagram.

MB: Yeah. So I have to say, amazing book. It resonated so strongly with me because I worked on billion dollar it projects and I fixed absurd numbers of them, or tried to, and I killed a few as a troubled project fix it guy. And I launched a bunch. But I think that what I was expecting more from the book was more the coda, the heuristics, because that seems like so much of your publication is about those types of heuristics. And so I recommend for people who finish chapter eight to keep reading. The Coda of heuristics is a very useful set of stuff to paste on a wall to remind yourself as you plan and think about projects and deliver projects don’t screw up. They’re very useful. Now, I’m going to have to be respectful of your time.

I know you and your firm are very busy, in demand, and your cycle of interviews should be increasing radically as this book comes out. So I always like to leave an open ended opportunity. We’ve been talking about the transformation, we’ve been talking about climate change, we’ve been talking about risks. But if you had just an open ended opportunity to give guidance to people based upon your perspective, what would it be?

BF: If I could say only one thing, it would be understand your base rates. And base rates are like your basic risks. Like what we talked about, people going to the casino. The casino. The base rates in a casino are the odds in the casino for the individual game. So theres a base rate for playing the roulettes, theres a base rate for playing blackjack and so on. I find that most people both doing projects and investing in projects dont understand what the base rates are. And that fits completely with behavioral economics. So this is, there’s something called the base rate fallacy. That’s our fallacy. We are hardwired not to get the base rates right. And that’s actually the most simple thing we can do, is to get the base rates right. And we know how to do this now.

Like we have the data for this, we know how to do it with reference, class forecasting and so on. So that would be my first thing. But there, but there’s many things. And even though you mentioned that the heuristics are in the codec, there are eleven heuristics, to be specific. And I agree. I really encourage people to get there. But we have also spread them out through the book. They pop up in different places in the book, also in context with specific examples and with specific people actually using them and being successful using them. So that’s another thing I would say, in addition to getting your base rates right, I would say get your heuristics right, start working on your heuristics. This is an individual thing.

Each master builder has his or her own set of heuristics, and I haven’t met a master builder that does not have heuristics. So that’s another thing. If you haven’t worked on your heuristics, start thinking about this and it might be a good place to start. In the code, it’s just a few pages. And as we say there, we put those heuristics in there to inspire your heuristics. So you can see which one do you resonate with and which one would you change, and you probably have additional heuristics that you would add to that. Listen, that’s another thing to do.

MB: Thank you very much. So I’m Michael Barnard, and my guest today has been Bent Flyvbjerg, the first BT professor of the said School of Economics at Oxford University. He also has a professorship at the IT University of Copenhagen. But those things are in aid of him being the world’s leading megaproject expert. He consults globally, he assists people. His intellectual capital on how to manage risk and programs is used globally, and it’s very applicable and very good news for the clean technology and the transformation we have to do. Ben, thank you so much for your time today. Really appreciate speaking with you, and I wish I had six more hours.

BF: Thank you. Likewise, Mike. And thank you for giving me this opportunity to talk about these things. Thank you so much.