Speaker 1: With Laurent Segoland from London and Gerard Reed from Berlin.
Speaker 1: This is Redefining Energy.
Speaker 2: Today on Redefine Energy, part our summer series. We re
Speaker 2: air all the episodes, but this one is fascinating because
Speaker 2: it's an interview by Michael Barnard on our sister show,
Speaker 2: Redefining Energy Tech with the great Professor Ben Fleiberg, the
Speaker 2: author of How Big Things Get Done.
Speaker 3: And Lauren was read that book last year or there before.
Speaker 2: Yeah, amazing, absolutely amazing. So listen to Michael Bernard interviewing
Speaker 2: Professor Ben Fleidberg.
Speaker 1: Welcome back to Redefining Energy Tech with your host Michael Barnard. Hi,
Speaker 1: welcome back to Redefining Energy Tech. I'm your host, Michael Barnard.
Speaker 1: This is a repeat of a discussion I had with
Speaker 1: Professor Ben Flobier, author of twenty twenty three's top selling
Speaker 1: business book, How Big Things Get Done. Listen in for
Speaker 1: their discussion of nuclear modularity, scaling and mega projects.
Speaker 3: Thank you very much, Thank you for having me.
Speaker 1: Now the excuse for having you because I gladly spend
Speaker 1: hours and hours and hours talking to the about a
Speaker 1: bunch of topics that sadly we don't have time for
Speaker 1: is the publication of your upcoming book with Dan Gardner,
Speaker 1: and so you know that book is How Big Things
Speaker 1: Get Done, and it explores your decades of research and
Speaker 1: experience in dealing with megaprojects, their failures, what works, and
Speaker 1: what doesn't. And today we're going to focus on some
Speaker 1: clean tech aspects out of that. But before we get
Speaker 1: into that, how did you end up being the person
Speaker 1: that is the go to person for governments globally when
Speaker 1: megaprojects need to be started effectively, when portfolios and megaprojects
Speaker 1: need to get moving, or when they just go off
Speaker 1: the literally in some cases.
Speaker 3: So, like you mentioned, I'm an economic geographer, and economic
Speaker 3: geography is a special branch of the economics that focus
Speaker 3: on what we call spacial economics. So you look at
Speaker 3: how how do how do economics play out in space?
Speaker 3: And that could be urban space or regional space, on
Speaker 3: national space, so you basically can look at what's going
Speaker 3: on on the surface of the planets in economic terms.
Speaker 3: And that's what I was raised on, you know, as
Speaker 3: a student, undergraduate, graduate PhD post of everything, And at
Speaker 3: one stage I started noticing that the citis and regions
Speaker 3: were being built in larger and larger chunks. So the
Speaker 3: projects that make up a city, to take that city
Speaker 3: as an example, just got larger and larger. And I
Speaker 3: noticed this early on and I identified it as a
Speaker 3: mega trend. You know, I said, okay, this is going
Speaker 3: to be the future. It's actually not planning that is
Speaker 3: so important as such, you know, which you talk about
Speaker 3: planning as lendings planning. It's much more what are the
Speaker 3: specific projects that a city decides to do and how
Speaker 3: they're delivered. That's what's going to be defining cities and
Speaker 3: actually regions and ultimately nations in the future. This is
Speaker 3: a long time ago. I saw this just by observing,
Speaker 3: you know, what was happening on the ground, including in
Speaker 3: my home country, which is Denmark, but also other countries.
Speaker 3: And I started looking around and found I couldn't find
Speaker 3: any data on this, even though already at that stage
Speaker 3: trillions and trillions of dollars were spent on these big projects,
Speaker 3: and nobody who was collecting data. You couldn't get data
Speaker 3: on them. So as a scholar, you know, this is
Speaker 3: an ideal of situation. That's There's that why the area
Speaker 3: on the map nobody has startied it before, let me
Speaker 3: get in there and study it. So that's how it started.
Speaker 1: Why was there in irritated? It strikes me as you know,
Speaker 1: it's an obvious insight, but why do you think that was?
Speaker 1: We've been building big stuff for thousands of years. Why
Speaker 1: haven't people been collecting even good anecdotal data.
Speaker 3: For For several reasons. One is that it's very hard
Speaker 3: to connect this data. I didn't know this at the beginning,
Speaker 3: but I got educated quick about how difficult it is
Speaker 3: to get high quality data. There's so much crap data
Speaker 3: out there, and in a big project, there are so
Speaker 3: many versions of data. So just to choose the right
Speaker 3: version of the data, which would be the version that
Speaker 3: has validity and reliability from a scholarly point of view,
Speaker 3: is a major job in its own right. So that's
Speaker 3: one thing. And that meant that they were only like,
Speaker 3: there might be some data on individual projects or a
Speaker 3: handful of projects, but there was nothing that would allow
Speaker 3: statistical analyscence, you know, for which you need a large example.
Speaker 3: And so it wasn't really on the ratar of scholars either.
Speaker 3: And practitioners are so in the here and now, they're
Speaker 3: very huge oriented. They're always thinking about the next project.
Speaker 3: Nobody thinks once you deliver a project. Actually, it's very
Speaker 3: very rare to have anybody stop and think, Okay, what
Speaker 3: can we learn from what we are what we just did?
Speaker 3: You know, why don't we connect some data on the
Speaker 3: outcomes and then see, you know, how the outcomes compared
Speaker 3: to the plans. This is almost never done. So this
Speaker 3: this is a combination of reasons, you know why, data
Speaker 3: that just were not available until we started collecting data.
Speaker 3: And it actually took us five years to collect the
Speaker 3: first data set of just two hundred and fifty eight projects,
Speaker 3: which was the largest data set in the world, you know,
Speaker 3: when we polish this in two thousand and two, you know,
Speaker 3: so this is just over twenty years ago now and
Speaker 3: today it's ludicrous to think that two hundred and fifty
Speaker 3: eight projects could be the largest data set the world. Now,
Speaker 3: just my group, you know, might see we have a
Speaker 3: data set that has now grown to over sixteen thousand projects,
Speaker 3: you know, which still is the largest database in the
Speaker 3: in the world. But that's how we started, and two
Speaker 3: hundred and fifty eight was actually enough to start doing
Speaker 3: statistical analysis and come up with some valid and reliable
Speaker 3: answers to questions like how much does it actually cost
Speaker 3: to deliver a big project, what's the likely cost over
Speaker 3: on the big project, how long does it take, what's
Speaker 3: the likely schedule over on? What are the benefits generated?
Speaker 3: And is it the benefits that were plans or do
Speaker 3: we get more benefits or less benefits than we're playing.
Speaker 3: So these were the kind of questions we could start
Speaker 3: rigorously analyzing after we got those first two hundred and
Speaker 3: fifty eight projects.
Speaker 1: Let's just take an example, like we connected over nuclear energy,
Speaker 1: and we'll get into that in a bit, but let's
Speaker 1: just take the history of nuclear generation buildouts. You know,
Speaker 1: I've looked at that, You've looked at that. Take Ontario,
Speaker 1: where I've lived many times. That place they had a
Speaker 1: provincial strategy and the richest largest province in Canada equivalent
Speaker 1: to Baden. It's not Rickenstein, but that big the biggest
Speaker 1: province in Germany. And they took twenty billion dollars off
Speaker 1: the books and shoved into general accounting at one point,
Speaker 1: and you know, fifteen years ago or so, they pushed
Speaker 1: it back into the utilities books. But it's now visible
Speaker 1: and they're trying to pay it down. But I look
Speaker 1: at France, and you know, if we take Macron, when
Speaker 1: he was in charge of the organization that should know this,
Speaker 1: has admitted that he could never figure out how much
Speaker 1: they actually spend because as national strategies or major political strategies,
Speaker 1: money kind of flows in in odd ways in different
Speaker 1: accounting systems. So how do you reconcile some of those
Speaker 1: challenges in a way that was academically sufficiently of quality.
Speaker 3: So we just look at that, and you're right, there
Speaker 3: are myriad ways that you can hide costs, take them
Speaker 3: off the books, or you can include them in the
Speaker 3: books obbitrarity, and that's a huge problem. So basically, we
Speaker 3: started this in detail for each and every project and
Speaker 3: see if we can make sense of me. Sometimes we can't,
Speaker 3: then we don't include the project. We have clear criteria
Speaker 3: that that data have to be valid and reliable, So
Speaker 3: if there's something dodgy that we can't explain, we will
Speaker 3: simply leave out that project because we're very, very teenly
Speaker 3: aware that we don't want to produde our database. Which
Speaker 3: is the problem with most data that consultants are using
Speaker 3: is that it's garbage in, garbage out that they are
Speaker 3: so they're so hungry for just having data that they
Speaker 3: will accept data even if the quality of the data
Speaker 3: might be a dubious quality of dubious status, and they
Speaker 3: include it anyway, And in that way you get a
Speaker 3: database that is not you know, really worth it. And
Speaker 3: I decided from the very start that we were not
Speaker 3: going to do that because I felt, you know, we're
Speaker 3: going to be spending a lot of time on this
Speaker 3: and that would be completely meaningless if we didn't know
Speaker 3: the validity and the liability about data, So we would
Speaker 3: not include anything that had unacceptable validity and reliability. That
Speaker 3: doesn't mean that you know, the data are perfect. There
Speaker 3: there's no such thing as perfect data. On big projects
Speaker 3: like like we're talking about here, there would always be
Speaker 3: things that you know could be better and that are
Speaker 3: not entirely clear. But still you know the day it
Speaker 3: would have to be above as thrashold where you could say, hey,
Speaker 3: this this data actually contains a really worthwhile information. It
Speaker 3: tells you know, the truth about what the costs were here,
Speaker 3: and it couldn't if it couldn't claim that, it wouldn't
Speaker 3: include it all the schedule, all the bit of its.
Speaker 1: Yeah, and it's interesting what you get up with because,
Speaker 1: as you say, sixteen thousand data points and now you
Speaker 1: can do statistical analysis. One of the things I thought
Speaker 1: was most fascinating in the book was, you know, you
Speaker 1: not only looked at costs and bud and schedule overruns,
Speaker 1: but you also looked at benefit of cruels and you
Speaker 1: actually did a fundamental piece of analysis to disprove a
Speaker 1: theory about unlocking creativity and doing stuff, even though it
Speaker 1: appears to be a bad idea.
Speaker 3: That insight was.
Speaker 1: I mean, zero point five percent of projects, if memory serves,
Speaker 1: actually come in on time, on a budget or close
Speaker 1: to it, and actually deliver benefits point five percent. Can
Speaker 1: you unpack that a bit and unpack that argument that
Speaker 1: you countered.
Speaker 3: Yeah, So the point five percent we call the iron
Speaker 3: law projects, and the iron law goes like this, over budgets,
Speaker 3: over time, on the benefits, over and over again. That's
Speaker 3: the Iron law. And about forty eight percent of projects
Speaker 3: projects come in on budget are better. About eight percent
Speaker 3: of projects come in on budgets and on time or better.
Speaker 3: And then you know the famous share point five percent.
Speaker 3: Those are the projects that come in on budget, on
Speaker 3: time and deliver the promised benefits or better. So that's
Speaker 3: how little it is. Half a percent. There's a there's
Speaker 3: a diagram in the book. And the interesting thing is
Speaker 3: that that half percent, you know, when we had to
Speaker 3: show that in the diagram, it's invisible because you need
Speaker 3: to show the one hundred percent, you need to show
Speaker 3: the forty eight percent, you need to show the eight percent,
Speaker 3: and then the half percent just becomes invisible. And that's
Speaker 3: pretty much a good metaphor, you know, for how things
Speaker 3: are in real life, that the products that tick all
Speaker 3: the boxes regarding success, which is on budget, on time,
Speaker 3: and on benefits, are better. I'visible. That's how few they are.
Speaker 3: I want to emphasize that it doesn't mean that they
Speaker 3: don't exist, especially when you started thousands of projects. Even
Speaker 3: half a percent will give you like a couple of handfuls.
Speaker 3: And that's what's really interesting to me is how on
Speaker 3: earth to the people who are doing these projects, how
Speaker 3: do they beat the odds? You know, because the share
Speaker 3: point five percent, that's your base rate, that's your objective
Speaker 3: base rate. When you look at these products like going
Speaker 3: to the casino, if you play the if you play
Speaker 3: the game of building a big project, your odds are
Speaker 3: shirru point five percent that you will win it. By winning,
Speaker 3: you define it as you know, being on budget, on time,
Speaker 3: and on benefits. So those are pretty steep odds, you know,
Speaker 3: and if you are going to go in and claim
Speaker 3: I can beat those odds, you better have something and
Speaker 3: I want to know what that's something is. And we
Speaker 3: spend a lot of time that in our research, and
Speaker 3: when we wrote the book, you know, picked the brains
Speaker 3: and started the data of people who actually beat the odds.
Speaker 3: Just as you would if you were in a casino,
Speaker 3: as you were interested in winning a casino. There's somebody
Speaker 3: walking around the casino and they beat the roulette, they
Speaker 3: beat blackjack, and so on consistently. You want to know
Speaker 3: what's going on there. And believe me, the casinos have
Speaker 3: detectives that are on this immediately and cameras that are
Speaker 3: on people. They'll be on this immediately and study what's
Speaker 3: going on here in a can see in a casino,
Speaker 3: it's usually fraud, you know, somebody's gaming the system of
Speaker 3: counting cards or whatever, and they will be expelled from
Speaker 3: the casino very quickly. So we were like those detectives
Speaker 3: and those cameras, we were watching all the people doing
Speaker 3: projects and finding who are the ones that actually are
Speaker 3: winning here, And we didn't kick them out. We actually
Speaker 3: sought them out and said, can we please talk, can
Speaker 3: we please look at your data, can we please look
Speaker 3: at your documentation, and so on, and then we figured
Speaker 3: out what is it that they're doing that they are
Speaker 3: successful at this.
Speaker 1: It's an interesting question speak institutional investors. Occasionally they reach
Speaker 1: out for, like you know, somewhat often contrarian perspectives on
Speaker 1: the decarbonization future. But one of the things that occurs
Speaker 1: to me is you've got a set of seventeen or
Speaker 1: twenty five categories of projects, and only five of those
Speaker 1: are in the sweet spot of usually delivering on time,
Speaker 1: on budget, and on you know, in delivering benefits. And
Speaker 1: you know, that's an easy list. I mean, transmission is
Speaker 1: in there, for example, and but you know then we
Speaker 1: kind of going together stuff Nuclear is way down the
Speaker 1: bottom of the list. You know, what are some interesting
Speaker 1: examples of things that you'll usually go wrong or usually
Speaker 1: go right in your perspective.
Speaker 3: This is for me, this is the most interesting result
Speaker 3: in the book, you know, And it's the first time
Speaker 3: we publish these, and we look at twenty five different projects.
Speaker 3: We present the data for those twenty five different projects,
Speaker 3: and we find, like you say, four or five projects
Speaker 3: type do very well and all the risks are doing
Speaker 3: rather poor to different degrees to be shure and be
Speaker 3: show different degrees. And so some of the successes are
Speaker 3: as you mentioned, transmission, and the most successful is actually
Speaker 3: solar power, you know, solar energy, that's the most successful.
Speaker 3: After that wind wind energy. So now we have solar
Speaker 3: wind transmissions. Wow, that's interesting because that's exactly what we
Speaker 3: need in order to solve the climate crisis. Right, So
Speaker 3: I just I went through but I saw this, I said, WHOA,
Speaker 3: what a result? How lucky? Can I mean? It might
Speaker 3: have been the opposite, you know, and at the opposite
Speaker 3: and which you also ask for what are the examples
Speaker 3: are really underperforming projects? You know, one of the worst
Speaker 3: is nuclear power, only only you know, surpassed in worseness
Speaker 3: by nuclear storage, you know, so nuclear is way at
Speaker 3: the other end. So basically in nuclear and solar at
Speaker 3: opposite ends of the scale. Yeah, but lots of other
Speaker 3: stuff is at the bad end of the scales. Who
Speaker 3: like the Olympic Games is one project type we study
Speaker 3: that's that's has terrible performance consistently it's not so hard
Speaker 3: to understand. I mean you always do it. In a
Speaker 3: new country. We talk about something called the eternal beginner's syndrome.
Speaker 3: You know that that it's always beginners who are doing this,
Speaker 3: so you don't get accumulated experience for the Olympic Games.
Speaker 3: Hydroelectric power same things. So big dams, whether they are
Speaker 3: hydroelectric or not. You know, big dams are really difficult
Speaker 3: to build and they also have bad performance airports, high
Speaker 3: speed rail, lots of other project types. So there it's
Speaker 3: a bad and at the other end we have these
Speaker 3: good performing, well performing types. And the secret to the
Speaker 3: difference is we also uncover that is actually fat jales,
Speaker 3: so thin tailed projects. Projects that have thin tail distributions
Speaker 3: for their performance are doing well. So they have more
Speaker 3: or less like a normal distribution of course and coursed
Speaker 3: overrun and scheduled, scheduled oberon and benefits, and that means
Speaker 3: that they're managing. So they are normal in that sings.
Speaker 3: They it is statistically right. The other projects that are
Speaker 3: going bad have fat chains. So what Ne's in talent
Speaker 3: calls next ones, and they are some by some people,
Speaker 3: they are considered outliers. We don't consider them outliers because
Speaker 3: they're actually part of the distribution. It's not like some
Speaker 3: some error, statistical error. It's actually real performance, real projects
Speaker 3: who performed like that, So they are part of the distribution,
Speaker 3: and they are extreme like you could have on IT projects,
Speaker 3: which is the worst project type. Regarding outliers. You know,
Speaker 3: if you look at the fat tail, the average in
Speaker 3: the fat tail is more than four hundred percent cost overron.
Speaker 3: That means that there are products with much higher cost
Speaker 3: over runs than the four hundred because that's an average, right,
Speaker 3: So in IT to find cost over runs of four
Speaker 3: or five six hundred percent is not unusual, and that's
Speaker 3: a completely different board game should be in, you know,
Speaker 3: compared to the Living Solar, where you never find that
Speaker 3: kind of cost of run, at least not so far
Speaker 3: in the data that we have seen.
Speaker 1: Yeah, it's interesting because I prefer the Michelle Wooker's gray
Speaker 1: Rhino metaphor completely understand why, you know, for a more
Speaker 1: Western origed book, which I think your book is. You know,
Speaker 1: black Swan has become the dominant metaphor for risk since
Speaker 1: Talent published, you know, around the same time as the
Speaker 1: subprime mortgage crisis. My experience with the book is that
Speaker 1: people have used it as an excuse to say we
Speaker 1: couldn't have possibly known about these things, as opposed to say,
Speaker 1: we ignored these things that were pretty obvious if we
Speaker 1: thought about them, and we didn't build a resilient system
Speaker 1: that would accommodate fat tail risks knowing that they're present.
Speaker 1: And so, you know, Wooker's metaphor, you you redefine, you
Speaker 1: define the way people should think about black swans in
Speaker 1: your book, and I recommend people read it obviously, but
Speaker 1: I would say that you're actually redefine it much more
Speaker 1: like Walker's gray rhino metaphor, which oddly it's much more
Speaker 1: dominant in Asia than it is in the way She's
Speaker 1: She's had massively more penetration there. It's been discussed at
Speaker 1: the highest levels of the Chinese pollup Borrow, for example,
Speaker 1: in terms of guiding their strategies. You can kind of
Speaker 1: see some of those long termism behaviors and the way
Speaker 1: they're structuring out stuff versus you know, some of the
Speaker 1: short termism that often democracies indulging around electoral cycles. We're
Speaker 1: seeing those kind of implications. So have you actually read
Speaker 1: a worker's book and have many examples or contrasts there
Speaker 1: that you you know, to to.
Speaker 3: Pull that apart. So the way I understand it is
Speaker 3: that the gray rhinos are less extreme than the lex ones,
Speaker 3: so that they are they are extreme, but they are
Speaker 3: not as extreme. Actually, our definition, we don't have to
Speaker 3: call it lex ones or gray rhinos or anything. Our
Speaker 3: definition is actually statistically. You know, statisticians have a very
Speaker 3: clear definition of what extreme values are. So there's something
Speaker 3: called extreme valued theory and statistics. And you define an outlier.
Speaker 3: So if it's tossed over run, for instance, would you
Speaker 3: would say it's a The technical definition is that it's
Speaker 3: one point five interquartile ranges about the third quartile. And
Speaker 3: that's you know, that's objective. That's just the ways that
Speaker 3: it is. This and they don't care what you call it,
Speaker 3: whether you call it gray or black or whatever it is.
Speaker 3: That's what's called an extreme value, an extreme value theory.
Speaker 3: As I understand, gray rhinos they come in, they're not
Speaker 3: necessarily that extreme, but they're still extreme enough to create
Speaker 3: lots of problems, you know, in practical policy and planning.
Speaker 3: And I would say the way we do it, we
Speaker 3: consider the whole range you're familiar with reference stars forecasting.
Speaker 3: I've seen in what you're writing, and if you read
Speaker 3: about the way we do that, you will know that
Speaker 3: we look at the total distribution, the total probability distribution,
Speaker 3: or whatever variable it is that we are forecasting. And
Speaker 3: I find that gives me really peace of mind because
Speaker 3: I know there's nothing I don't look at, you know,
Speaker 3: So whether it's gray or black or what it is,
Speaker 3: it's being considered. And we do pay special attention to
Speaker 3: the most extreme values because we know that these are
Speaker 3: the most damaging. If you end up in the tail,
Speaker 3: you are in trouble, and you are not only in
Speaker 3: a little trouble, you're in big trouble. And it's very
Speaker 3: hard to, you know, protect yourself against through contingencies because
Speaker 3: the contingencies just have to be too high. You will
Speaker 3: never get those kinds of contingencies. It's not even rational
Speaker 3: within an organization to allocate that much contingency to anyone
Speaker 3: project because it means you can't use the resources for
Speaker 3: other projects. Right, So what we recommend is that when
Speaker 3: that like eighty percent of the probability distribution, you can
Speaker 3: protect yourself with contingencies, and we calculate the exact size
Speaker 3: of those contingencies, and then we say the remaining part
Speaker 3: of the probability distribution, you really go you got to
Speaker 3: go in and mitigate so that doesn't happen. We call
Speaker 3: that black sworn management. And then then there's slogan of
Speaker 3: blacksworan management is cut the tail. You need to cut
Speaker 3: the tail. And how do you cut the tail? You
Speaker 3: cut the tail through mitigation. So let's take a really
Speaker 3: simple example like Fukushima, you know, and you need to
Speaker 3: protect the nuclear power plant against a tsunami wave. Simple
Speaker 3: you build a wall that's tall enough and then that's it.
Speaker 3: You know, that's cutting the tails. Or move it uphill
Speaker 3: because that it uphill, you know, that's even easier, even easy.
Speaker 1: They build the plant downhill from a tsunami high level
Speaker 1: marker from the seventeenth century.
Speaker 3: So we go through an exercise like this, and of
Speaker 3: course most of the time it's not a tsunami wall
Speaker 3: that is the solution. I'm just mentioning that because it's
Speaker 3: so easy to understand, or building in a further in
Speaker 3: country or up you know, at a at a higher
Speaker 3: level and with different projects and project types. You know,
Speaker 3: mitigation means different things, but you can always sit down
Speaker 3: and think, how do I cut the chain. It's the
Speaker 3: most it's the most valuable exercise you can go through
Speaker 3: when you're delivering a project. It's very exciting and stimulating
Speaker 3: too when you get so this is something you do
Speaker 3: like a whole team of people who are experts in
Speaker 3: what you are, what you're talking about here, and they
Speaker 3: just sit down and for whatever time it takes, goes
Speaker 3: through like how do we get that tail off? How
Speaker 3: do we cut that tale? Are we confident that the
Speaker 3: tail is now cut? And come up with all the
Speaker 3: different issues that could help cut the team.
Speaker 1: Well, it's interesting because then I know you worked with
Speaker 1: Daniel Canneman, and I totally envy the two different academics
Speaker 1: who have had the opportunity to work with Cannaman. It
Speaker 1: just must be such a delightful person. And that's where
Speaker 1: I first ran across your work. In Cannonan's Thinking Fast
Speaker 1: and Slow and Cantman's Thinking Fast and Slow, he talks
Speaker 1: about the precursor kind of thinking behind reference class forecasting,
Speaker 1: but he also articulates the use of pre mortems as
Speaker 1: a mechanism for doing that. And I kind of was
Speaker 1: reading your book and saying, oh, he's not mentioning pre mortems.
Speaker 1: The reference class forecasting is excellent because it gives you
Speaker 1: a statistical view of the actual risk and the variability
Speaker 1: of the fat tales for your project class. But the
Speaker 1: pre mortem allows you to cut the tail. But you
Speaker 1: don't use that term. You call it cutting your tail,
Speaker 1: which is probably more evocative. Anyway, it's a process.
Speaker 3: Yeah, I think that I'm I seem to remember that
Speaker 3: we do mention the pre morgim somewhere so but anyway,
Speaker 3: whether we do or not, I do think pre morgons
Speaker 3: are very important methodology, very legitimate methodology. And yes, Canaman
Speaker 3: has been extremely helpful for my thinking. He's been very generous,
Speaker 3: you know, and giving me feedback and so on, and
Speaker 3: that has been very helpful obviously when you're using his
Speaker 3: ideas and that really works. And you probably saw his
Speaker 3: latest book with co authors called Noise and now, Yeah,
Speaker 3: he's using a more general term in that book and
Speaker 3: in previous publications after thinking fast and show cause it.
Speaker 3: It's decision hygiene. So I need a certain hygiene when
Speaker 3: you're making decisions, and typically humans just skip thatts we
Speaker 3: have no decision higene, We have this thing called availability bier.
Speaker 3: So we just jumped straight to the thing that just
Speaker 3: pops into our minds, you know, and then we start
Speaker 3: working on that and we feel that we've got going.
Speaker 3: We just we have such a birch. I don't know
Speaker 3: what it is is, but we seem to be hardwired
Speaker 3: just to get going with things and not to sit
Speaker 3: back and think and reflect over But what is it
Speaker 3: that we are doing here and think it through before
Speaker 3: we get going. So we have this availability bias that
Speaker 3: tends to get us going, and that the bypasses the
Speaker 3: decision hygiene, and decision hygiene is the solution to that problem.
Speaker 3: And decision hygiene is pre Morton explains, and so pre
Speaker 3: mortg is an example of decision hygiene, and the reference
Speaker 3: das forecasting is another example of decision hygene. And there
Speaker 3: are all That is.
Speaker 1: One of the most funny I've written about this in
Speaker 1: the past because I read Canam and I dealt with
Speaker 1: cognitive scientists like John Cook who's the PhD cognitive scientists
Speaker 1: behind Skeptical Science the Timate Change myth debunking site, and
Speaker 1: Stephen Lewandowski is PhD advisor. The availability bias that always
Speaker 1: amuses me is that so many thought leaders around decarbonization
Speaker 1: are North Americans, and they tend to be affluent and
Speaker 1: live in suburbs and have solar panels on their roots,
Speaker 1: and so their availability bias leads them to assume that
Speaker 1: rooftop solar can be a more impactful solution than it is. Now,
Speaker 1: do you live in Copenhagen itself? I live in Copenhagen
Speaker 1: itselfias in Oxford and Copenhagen. It's a bunch of five
Speaker 1: story apartment buildings where the population density is much more
Speaker 1: like the rest of the world than like North American suburbs.
Speaker 1: And so you know, I live in much higher density
Speaker 1: downtown Vancouver. I every once in a while at count
Speaker 1: I think it's nineteen eighteen story plus condo buildings are
Speaker 1: visible from my windows in my home office. Right, So
Speaker 1: we have I think thirty six hundred people on my block,
Speaker 1: for example, And this is much more like an Asian
Speaker 1: pattern of living. It's much more like the population density
Speaker 1: of Paris. But the availability bias of people who like
Speaker 1: distribute energy, they see detached homes, so they seem a
Speaker 1: lot more people in the world living detached homes.
Speaker 3: Just one of those interesting things. And that's the way
Speaker 3: we are. I mean, like like Canamans is in his book,
Speaker 3: like his life has been spent on studying these biases,
Speaker 3: but he's getting shipped up by them all the time anyway,
Speaker 3: you know, And I think we just have to realize this.
Speaker 3: That's no excuse for letting them run rampant, obviously, quite
Speaker 3: the opposite. It means that we really have to have
Speaker 3: this decision. I see. And if we don't do that,
Speaker 3: we're not doing our jobs. Yeah.
Speaker 1: I spent a lot of time figuring out what the
Speaker 1: denominators are and then you know, figuring out scale up.
Speaker 1: But so let's go back to a specific example. We
Speaker 1: talked about the the projects which the project classes like
Speaker 1: wind and solar, which tend to be very you know,
Speaker 1: get on time, get on budget, and achieve benefits versus nuclear.
Speaker 1: And the reason you know, you and Dan Gardner your
Speaker 1: cook originally reached out to me was because I published
Speaker 1: on the natural experiment of those three technologies in China
Speaker 1: over a dozen years. I've been looking at that since
Speaker 1: twenty fourteen. You included someone like you, You extended it
Speaker 1: in time and you know, transform some of the stuff
Speaker 1: because it made more sense. But what do you talk
Speaker 1: about what you observe from the experience in China and
Speaker 1: other places about why wind and solar are so successful
Speaker 1: and nuclear ism.
Speaker 3: Yeah, and first let me say we couldn't believe our
Speaker 3: luck when we came across that part of your work,
Speaker 3: because that's that natural experiment that China is exactly what
Speaker 3: we needed in order to, you know, get a decision
Speaker 3: on whether nuclear works, basically, because people usually say that
Speaker 3: China would be the place where it would work if
Speaker 3: it works, you know, and you just had the data
Speaker 3: to show that so and obviously your data showed that
Speaker 3: it doesn't work even in China if by working we
Speaker 3: mean being scaled up to the level that it needs
Speaker 3: to be scaled up to if it's going to be
Speaker 3: a major fix or the climate crisis. And you compare
Speaker 3: directly to wind and solar, and you show that they
Speaker 3: scale up much faster. Okay, So we explain why in
Speaker 3: the book. We say, okay, why why is that the case?
Speaker 3: That's that's kind of strange, you know, on first side,
Speaker 3: why would it be so different? But it relates back
Speaker 3: to what I said earlier, that nuclear is fat tailed
Speaker 3: and the wind and solar are thingsade, so they're much
Speaker 3: more managedble and they scale much better, much faster, much
Speaker 3: more easily than nuclear. Then we went deeper and we
Speaker 3: looked at what's called learning curves, which basically our courage
Speaker 3: that show, you know, the more you do a thing,
Speaker 3: you know, how much easier does it get. And by
Speaker 3: easier you often say cheaper, it's gets cheaper and faster.
Speaker 3: So and this is our normal experience as human beings.
Speaker 3: Just think if you're doing some handiwork around your house.
Speaker 3: You start on a Saturday morning, you need to get
Speaker 3: something done. Everything feels wrong, you know, the tools in
Speaker 3: your hand and whatever you're doing, but you get going
Speaker 3: and you get some experience with what it is that
Speaker 3: is your your task that day, and even within that
Speaker 3: kind of a short period of time, you get better
Speaker 3: at what you're doing. Now as you expand that and
Speaker 3: think about doing it day after day after day, you
Speaker 3: do something that our general experience is that it becomes
Speaker 3: easier to do something the more times we do it. It
Speaker 3: becomes cheaper to do something the more times we do it.
Speaker 3: It becomes faster to do something the more times we
Speaker 3: do it. Now, that's exactly what happens has happened with
Speaker 3: solar and wind. You know that we have become much
Speaker 3: much better. It used to take much longer even to
Speaker 3: put up a single wind turbine that it does now.
Speaker 3: You know, you put up turbine, say you know, one
Speaker 3: a day now and you build. So this is actually
Speaker 3: the first time I've ever seen multi billion dollar projects
Speaker 3: being built in less than a year. No, those are
Speaker 3: wind files. That's unusual. I would say usually you would
Speaker 3: have at least at fast makeup project. It's four or
Speaker 3: five years. That's fast. And here you have in the
Speaker 3: most difficult waters on the planet. This is also wind
Speaker 3: I'm talking about that is being built in the Irish
Speaker 3: Sea and the North Sea, which are both part of
Speaker 3: the North Atlantic, which is like part of the world
Speaker 3: as you you don't want to be out in the winter,
Speaker 3: like basically you can't do anything from October till April
Speaker 3: March April. And nevertheless, you know now in those seven
Speaker 3: months that they have to do things, they will actually
Speaker 3: put up a multibillion dollar wind farm in that period
Speaker 3: of time. That is totally off the chart. It's being done.
Speaker 3: And this is through this positive learning that I'm talking
Speaker 3: about getting better by doing it over and over and
Speaker 3: over again, and sola even more so because it's even
Speaker 3: more modular than wind. I usually call it legos. That's
Speaker 3: why the last chapter in our book is called what's
Speaker 3: your Lego? That's what you need to do when you're
Speaker 3: doing things. You need to have a lego in order
Speaker 3: to be to be effected. And you know, a wind
Speaker 3: turbine is four pieces of lego. There's a foundation, there's
Speaker 3: a tower, there's a nat shell that's the turbine itself,
Speaker 3: and then there are the blade or the wings that
Speaker 3: it's called sometimes also that click onto the cell, and
Speaker 3: that's that's your win turbine. So it's like click, click, click,
Speaker 3: and there it is. And Okay, those are big pieces
Speaker 3: of lego, so there are some difficulty in getting them
Speaker 3: out to see and there might be a lot of
Speaker 3: wind when they're raising them. So they have a lot
Speaker 3: of strong tools and so on, powerful tools, big tools
Speaker 3: to get these things done. But basically that's the way
Speaker 3: it's done. And it's because it's done like lego, click click, click,
Speaker 3: over and over that it's so efficient and that we
Speaker 3: get these economies of scale and economies of learning. So
Speaker 3: that's wind and solar at the other end back to nuclear.
Speaker 3: It turns out when you start looking at the learning
Speaker 3: cursion nuclear, it's the exact opposite. And this is weird.
Speaker 3: This is actually something that should not be happening, but
Speaker 3: it is happening. The more you do nuclear, the more
Speaker 3: difficult it gets. That's called negative learning. So you do
Speaker 3: want nuclear plants, and you find out this is actually
Speaker 3: more then we thought. For instance, you know, getting the
Speaker 3: safety requirements right is much more difficult than we had estimated.
Speaker 3: So now that we're doing an estimate for the next
Speaker 3: nuclear plant is actually going to be more expensive than
Speaker 3: the first one because now we have learned these things
Speaker 3: about safety, and we build that into the next estimate,
Speaker 3: and then we try again that's the second one. So
Speaker 3: you build the second nuclear plant at higher court takes
Speaker 3: longer because it's more difficult, and so on. What happens.
Speaker 3: Fukushima happens. You know, you have or three mile Island
Speaker 3: or Chernobuild. You know, you have an accident. All of
Speaker 3: a sudden, the rules and regulations change and they are
Speaker 3: being rammed up. You get much higher safety standards that
Speaker 3: you have to live up to. So now all of
Speaker 3: a sudden, the standards have gone up and you have
Speaker 3: to live up to those So now when you then
Speaker 3: the next nuclear power plant, the third one, it gets
Speaker 3: even more expensive because you have to live up to
Speaker 3: these standards. Right. That's negative learning. That's the more you
Speaker 3: do it, the more expensive it gets. And that is
Speaker 3: what has happened with nuclear over the past several decades,
Speaker 3: except maybe in one place, and that is Korea, but
Speaker 3: all the countries for which we have data, it looks
Speaker 3: like negative learning is the case that it gets more
Speaker 3: and more difficult, it gets more and more expensive. In fact,
Speaker 3: some of those nuclear power plants are causing bankruptcies for
Speaker 3: the companies that are building, like Westlinghouse with bankrupt because
Speaker 3: of the nuclear reactors that couldn't be built to the
Speaker 3: budgets and to the time that was in the United States.
Speaker 3: In Europe we have to we have to have a
Speaker 3: few more now. But the nuclear reactors that have progressed
Speaker 3: and should be close to being finished now they are
Speaker 3: not being finished.
Speaker 1: You know.
Speaker 3: They there's always a new story of why there's another
Speaker 3: delay and why the body is going up. So that's
Speaker 3: negative learning for you. We just don't have anything on
Speaker 3: the ground in North America and in Europe that would
Speaker 3: encourage us to think that we can deliver nuclear cheaper
Speaker 3: in the future. So that's why nuclear looks the way
Speaker 3: it does in our data. And that's why. And this
Speaker 3: is purely for economic reasons. Many people think this is
Speaker 3: something ideological being against nuclear. It's not even about nuclear waste.
Speaker 3: That's not even taken into account. This is surely economics
Speaker 3: of building nuclear power plants. On that criterion alone, nuclear
Speaker 3: is losing out. Yeah, it's interesting I characterize. Here's what
Speaker 3: I characterize is the success requirements for a nuclear rollout.
Speaker 3: And this is before I write your work, which you know,
Speaker 3: confirmation bias suggests we think identical on this, because of
Speaker 3: course what you wrote confirms what I think. One of
Speaker 3: those other biases that canon points out, which I try
Speaker 3: hard to overcome. But the articulation I make is that
Speaker 3: it's possible to do gigawatt scale nuclear reactor buildouts under
Speaker 3: the following conditions with reasonable experiences. First, you have to
Speaker 3: do lots of dozens in a country or a region. Second,
Speaker 3: it has to be a national strategy so that you
Speaker 3: get override over a whole bunch of local regulatory things
Speaker 3: that's typically related to to nuclear weapons build out or
Speaker 3: capacity to build out so the federal government can step
Speaker 3: in and throw its weight around. Third, it has to
Speaker 3: be exactly the same design. You have to avoid the
Speaker 3: desire to do bespoke engineerings for a specific site where
Speaker 3: the technology you just have to do dozens of the
Speaker 3: same thing. There was a period in the United States
Speaker 3: where the light pressurized water reactors were built in sufficient
Speaker 3: quantities that it worked out. And this gets to another
Speaker 3: point you make in the book was that you need
Speaker 3: a master builder and their team. Building the capacity of
Speaker 3: resources who are certified and security credentialed enabling them to
Speaker 3: work on nuclear plants is deeply non trivial. Like Holland
Speaker 3: is looking at a nuclear reactor and they have nobody
Speaker 3: who has any of those skills and where are they
Speaker 3: going to get them from DF Well, let's think.
Speaker 1: Working out really well in Hinckley. And so that master
Speaker 1: builder and their team of experienced people and shared stuff
Speaker 1: has to be done in a short period of time,
Speaker 1: which is getting introduced in the next subject, small modular reactors.
Speaker 1: They have to be built as components at the maximum
Speaker 1: viable scale for the physics of it. Right. So wind turbines,
Speaker 1: as you mentioned, are very big these days. Onshore the
Speaker 1: average is two point six megawatts. Offshore, it's much higher,
Speaker 1: and that's a logistical choice.
Speaker 3: If they could.
Speaker 1: Build wind turbines the size offshore wind turbines and get
Speaker 1: them along roads and on railways be hinder bridges, they would,
Speaker 1: But they can't deliver the blades and math and the
Speaker 1: nay cells to the sites onshore, but they can offshore.
Speaker 1: So that's why we're now seeing the in Wind project,
Speaker 1: which has been going on for decade decade or so.
Speaker 1: Now they're edging up towards twenty megawant individual wind turbine, massive,
Speaker 1: massive piece be You can deliver that offshore and build
Speaker 1: them in a port area and put them on ships
Speaker 1: that are special tenders. And then you have automated solutions
Speaker 1: heavy lift automation which enables you to construct them. But
Speaker 1: wind turbine it's getting bigger. It's not like we left
Speaker 1: wind turbine small because we need to maximize the physics
Speaker 1: of them. Now nuclear reactors from a thermal generation perspective,
Speaker 1: coal plants want to be bigger because that boiler and
Speaker 1: then generating the steam is more efficient. The bigger they
Speaker 1: get up to the maximum one to one point two
Speaker 1: gigawats and nuclear reactors are just coal plants with radiation
Speaker 1: instead of coal plants with CO two. Right, it's just
Speaker 1: thermal generation. So small modular reactors want to get that
Speaker 1: modularity and constructibility and deliver manufacturabilibility, deliverability, but they don't
Speaker 1: they forego the thermal dynamics, so they lose that efficiency there.
Speaker 1: And it's an interesting question for me. Have you seen
Speaker 1: other examples where modularity has trumped the physics in your
Speaker 1: data sets or do you have a perspective on that angle.
Speaker 1: I think it's an unknown question to me. I think
Speaker 1: it's significant, but I haven't done the math sufficiently ever
Speaker 1: seen another data set where smaller works outside of small
Speaker 1: onshore wind energy where the limits are logistics.
Speaker 3: So when you when you say that smaller works, do
Speaker 3: you mean that smaller works even when there is you know,
Speaker 3: so a larger level you know, like the like the
Speaker 3: one one point two giga what you're talking about.
Speaker 1: Yeah, physics tells us we.
Speaker 3: Haven't studied that. It's a very good point. It's very
Speaker 3: interesting to me, and and I understand your argument for
Speaker 3: small modular reactions, And all I can say is the
Speaker 3: nuclear industry must hate you because this is the only
Speaker 3: hope in my That's what I say. You know, if
Speaker 3: nuclear is going to work, it's going to work. It
Speaker 3: would be small, modular reactions. But now you're saying, like, no,
Speaker 3: that's that's it's not going to work there. You even
Speaker 3: say it's especially not going to work there, because that
Speaker 3: goes against the basic physics of the whole thing in
Speaker 3: terms of where is the sweet pot as spots of
Speaker 3: prenotting size. Right, Well, if if if you're right, then
Speaker 3: they I don't hesitate, you believe that you are, and
Speaker 3: then I would say it's game over for nuclear.
Speaker 1: Yeah, it's interesting because I've looked at data sets, you know,
Speaker 1: like yours, and the initial reactors being built in the
Speaker 1: fifties and sixties were tiny. They were much more like
Speaker 1: the ones on nuclear powered submarines and nuclear powered ships.
Speaker 1: Because guess where they nuclear industry started. They started mostly
Speaker 1: in the United States, and they repurposed their PWRs for
Speaker 1: electrical generation. But they found the electricity was really expensive
Speaker 1: at that scale, so they made them bigger, and then
Speaker 1: the electricity dropped, and then negative learning occurred and they
Speaker 1: tried to innovate instead of just building the same thing
Speaker 1: over and over again. They kept saying we could make
Speaker 1: it better, and then it all blows up again from
Speaker 1: a fast budget perspective. And so that's kind of that
Speaker 1: interesting thing right now. I see the small modular reactors
Speaker 1: getting bigger and bigger and bigger because they're doing the
Speaker 1: math and realizing small doesn't work. So it's fascinating to
Speaker 1: watch it balance back and forth. I think there's an
Speaker 1: optimal size in there, but I don't know what it is.
Speaker 1: I think it's over three hundred megawats per reactor. China,
Speaker 1: for example, or in India for example, has a bunch
Speaker 1: of reactors, can do Canadian design reactors that are three
Speaker 1: hundred megawaks or so maybe there's a sweet spot in there.
Speaker 3: I don't know.
Speaker 1: It's an interesting question, and I haven't seen a counter
Speaker 1: example to that. But I do have one counter example
Speaker 1: which I think is interesting, and that's the next topic,
Speaker 1: which is pumped hydra storage versus batteries, right because there's
Speaker 1: a really interesting one. You know, pumped hydrat storage. They're
Speaker 1: billion dollar plus projects. You have to you have to
Speaker 1: tunnel a ten meter diameter pen stock up to eight
Speaker 1: kilometers through solid rock and you have to build a
Speaker 1: reservoir four hundred meters or higher below the lower reservoir,
Speaker 1: and you have to put in a you know, four
Speaker 1: to twelve pumps or counter that are regenerative pumps. And
Speaker 1: building that cunnel thirty ten meters in diameter through kilometers
Speaker 1: of rock is non trivial. That's the megaproject part of
Speaker 1: pump hydro. Everything else is components and trivial stuff, and
Speaker 1: how to build reservoirs, you know, how to We can
Speaker 1: buy pumps off the shelf, We can buy the electronic
Speaker 1: control systems off the shelf. So that's thing one. Thing two, though,
Speaker 1: is batteries. Batteries are absurdly modular, like Tesla and Barzilla
Speaker 1: and all these other companies are now delivery containers full
Speaker 1: of cell based batteries, you know, thousands or hundreds of
Speaker 1: thousands of small, tiny modular components plugged into a repeatable
Speaker 1: tattern in a deliverable container framework. And so that's going
Speaker 1: off the shelf. So this is an interesting aspect. But
Speaker 1: I always like to tell people this is kind of
Speaker 1: that introductory piece, and I have a big question for
Speaker 1: you at the end of this. Pumped hydro is a
Speaker 1: I would say it is modular in the sense that
Speaker 1: it uses multiple turbines and knows how to do that,
Speaker 1: and it's highly commoditized hardware non you know, there's not
Speaker 1: a lot of innovation and pump hidri. Second, it's by
Speaker 1: far the largest This is stuff people don't know. It's
Speaker 1: by far the largest energy and power grid storage in
Speaker 1: operation globally today, like orders of magnitude higher than anything else.
Speaker 1: And that's because we built a lot to give coalon
Speaker 1: nuclear plants something to do at night, to justify those
Speaker 1: investments in gigawatt scale generation. The last point is it's
Speaker 1: also by far this is another weird unknown to me
Speaker 1: in the power and energy world. It's also by far
Speaker 1: the largest power and energy form of grid storage under
Speaker 1: construction today. Like we hear about all these batteries going in,
Speaker 1: but the batteries are going in twenty to forty to
Speaker 1: one hundred megawatts. And I look at Mark Wilson's, you know,
Speaker 1: across the water from you in Scotland. Mark Wilson of
Speaker 1: Intelligent Lands Investments has three pumped hydro facilities that he's
Speaker 1: developed and he's currently in the process of selling to
Speaker 1: people who will construct them. He's done all the transmission
Speaker 1: interlocks and got all that stuff done. But Turkey's nest
Speaker 1: on top of a hill next to lock nests and
Speaker 1: Lockness is a lower reservoir, and those three have two
Speaker 1: point five gigawants of power capacity and sixty gigawant hours
Speaker 1: of storage, which dwarfs all the battery projects in Europe. Right,
Speaker 1: And that's one small developer in one small country.
Speaker 3: So it's an interesting question.
Speaker 1: Where does the modularity and that repeatability and that fat
Speaker 1: tail stuff have And do you have pumped hydro projects
Speaker 1: in your data set yet.
Speaker 3: Not as a separate category. We have dams, as I mentioned,
Speaker 3: as a category, but we haven't singed it out pumped
Speaker 3: hydro yet, but we'd be very interested. Very often the
Speaker 3: way new projects get into the data set is that
Speaker 3: somebody from the outside contact we would like to work
Speaker 3: on this, you know, will you help us, like explain
Speaker 3: to us how you build a good data set, and
Speaker 3: then we help them and we build a good data
Speaker 3: set for whatever it is, what asset they're interested in,
Speaker 3: in this case, pumped hydro, and I'd be very interested
Speaker 3: in working on that, but we don't have the data yet.
Speaker 3: My advice though, would be to people doing pumped hydro,
Speaker 3: and they probably already know this and don't need my advice,
Speaker 3: but it would be. And you know a lot of
Speaker 3: organizations are contacting us now trying to help them with
Speaker 3: the question, how do we mode modularize what we're doing further?
Speaker 3: Even if what they're doing is very very modularized already,
Speaker 3: how do we modelize further? Yeah, and that's the key question,
Speaker 3: And that's what I would say if you're doing pumped hydro,
Speaker 3: that's what you need to focus on. The thing that
Speaker 3: immediately gets my attention is like digging is involved involved,
Speaker 3: and we know anything that involves digging is really risky.
Speaker 3: You know, when you start digging, you don't know what
Speaker 3: you find, and a lot of the fat tails actually
Speaker 3: in construction come from digging and not knowing what you're
Speaker 3: going to hit on the ground. So that's something that
Speaker 3: needs to be sought through carefully for pumped hydro. How
Speaker 3: do we avoid the risks the unique risks that are
Speaker 3: that I involved in digging. Yeah.
Speaker 1: No, I've spent a lot of time randomly looking at
Speaker 1: tunnel boring machines and the multiple failures over the years
Speaker 1: as they run into much harder chunks of rock, you know,
Speaker 1: igneous intrusions from blow and fault lines and water and stuff.
Speaker 1: You document few of those, and that's kind of an
Speaker 1: interesting thing, because pump tidra is an interesting question, you know,
Speaker 1: I encourage you to think about it simply because it
Speaker 1: is so big and it gets a little pressed. It's
Speaker 1: one of those hiding under the surface of the battery
Speaker 1: hype and batteries are amazing and I love them. I
Speaker 1: recommend you know. I've published projections on global storage through
Speaker 1: twenty sixty and you know they're strongly present. But I
Speaker 1: think pump tidra is going to win, except there's this
Speaker 1: nagging question for me about the modulary perspective.
Speaker 3: So well, it doesn't matter if it's working, you know
Speaker 3: it's going to win, especially given the fact that it
Speaker 3: can scale to the level that you were talking about there.
Speaker 3: I actually I consider pumped hyd group and all the battery.
Speaker 3: You know, it is a battery in essence.
Speaker 1: Yeah, it gets rid of a lot of the fat
Speaker 1: tail risks. The Australian National University study from six projects
Speaker 1: years ago by Matt Stocks very interesting. What he did
Speaker 1: is I was looking at machine learning and clean tech
Speaker 1: solutions globally. That one popped up on my radar and
Speaker 1: I talked to Matt Stocks and what he'd done was
Speaker 1: he'd actually not used machine learning at all, which was interesting.
Speaker 1: He'd just taken a GIS data set and queried a
Speaker 1: bunch of questions. And the questions were two sites within
Speaker 1: this much horizontal distance kilometers, you know, a couple of
Speaker 1: kilometers horizontal distance, at least four hundred meters of vertical distance,
Speaker 1: because it's mgh. It's just a it's just you know,
Speaker 1: you want higher for more void, more maths to get
Speaker 1: better storage. And then he said it has to be
Speaker 1: too near a transmission, it has to be off protected lands,
Speaker 1: so getting rid of two or three long long tail risks,
Speaker 1: and you know, then projected there's Huberd times the resource
Speaker 1: the worst case scenario for storage global.
Speaker 3: Wow. So it's an intro.
Speaker 1: I know, it's just a huge resource. And what that
Speaker 1: means is if you have a five hundred megawat meter
Speaker 1: head height difference and a gig a liter of water,
Speaker 1: that's a gigawant hour of storage. Yeah, it's just a
Speaker 1: huge volume, and it's just water is a cheap commodity.
Speaker 1: I'm beginning to question for myself because about this cognitive
Speaker 1: dissonance between the modularity stuff which I deeply internalize and
Speaker 1: my preference. I'm tidro and the question is can I
Speaker 1: justify it by saying water is the ultimate modular resource?
Speaker 1: H That's pretty tough, yeah, anyway, But what I what
Speaker 1: I think you know is that this is good news.
Speaker 3: And this is one of the things that struck me,
Speaker 3: you know when we wrote the book is that, like
Speaker 3: I mentioned earlier, we saw, Wow, it actually turns out
Speaker 3: that solar and wind are thin tailed and therefore easy
Speaker 3: to deliver it. How lucky is that, you know, giving
Speaker 3: the problems we have with the climate crisis, and now
Speaker 3: you're saying the same for pumped hydro. So those are
Speaker 3: really good news. You know that there actually are things
Speaker 3: there that can really be scaled and it's exactly what
Speaker 3: we need. Right So I'm actually quite optimistic about being
Speaker 3: able to solve the problems with the climate crisis given
Speaker 3: the technologies we have. If we can get our stuff
Speaker 3: together to skate it fast enough. That's really what it's about.
Speaker 3: That we need to be able to do this at
Speaker 3: a scale and at a speed that is unprecedented.
Speaker 1: And I'd like to lean into this now because you
Speaker 1: talked about transmission, and you also talk about two examples
Speaker 1: of high speed rail. And you know, I look at
Speaker 1: high speed rail and I say that should be like transmission.
Speaker 1: It's a linear project. It has repeatable pieces along the way,
Speaker 1: have to prepare a foundation, but it's just a repetitive
Speaker 1: process of putting in place. So why does high speed
Speaker 1: rail fail in your examples and in your data set?
Speaker 1: And why doesn't it? And one of the questions I
Speaker 1: have to ask you is does your data set include
Speaker 1: the forty thousand kilometers of high speed rail that China's built?
Speaker 3: No, it doesn't. It does include high speed rail, but
Speaker 3: not the forty thousand kilometers China has built. Not because
Speaker 3: we didn't want it. We'd actually asked for it. But
Speaker 3: you know, it's difficult to get data in China. That's
Speaker 3: why we were so happy about your data that you
Speaker 3: were actually able to get them. We did, We did
Speaker 3: get through the back door and and and gods, you know,
Speaker 3: data for Chinese transport infrastructure projects through the World Bank.
Speaker 3: You know, the World Bank is working in China, and
Speaker 3: they have certain requirements regarding getting data on the products
Speaker 3: that they fund, which meant that in DC they had
Speaker 3: they had data on some projects like just under one
Speaker 3: hundred projects in the in China and but not not
Speaker 3: from the high speed rain network. This is conventional rail
Speaker 3: and rail and road projects that we've got data for.
Speaker 3: So we don't have the data for China high speed rail.
Speaker 3: We do have data for quite a lot of high
Speaker 3: speed rail around the world, and it's not performing well.
Speaker 3: It's not as bad as nuclear or the Olympics. It's
Speaker 3: sort of in between and sort of midway in between.
Speaker 3: It's also not as bad as ig projects or defense projects.
Speaker 3: Defense protects are also terrible aerospace projects. It's somewhere in between.
Speaker 3: And I would say it's because again of the digging.
Speaker 3: You know, there's a lot of digging involved in building
Speaker 3: a high speed rail line, much more than in transmission
Speaker 3: is easy. You know, transmission is basically following the surface
Speaker 3: of the earth, or if it's if it's being if
Speaker 3: they're digging in laws, that would be because of landscape reasons.
Speaker 3: So in the city you put it underground. But it's
Speaker 3: not like it's not like a board tunnel, you know,
Speaker 3: it's just serious digging. So it's much more limited digging
Speaker 3: and if there is any digging at all with transmission,
Speaker 3: whereas with the high speed rail it's very very serious digging.
Speaker 3: It's it'sunnel boring and also bridges. So there's a lot
Speaker 3: of bridges and a lot of tunnels on any high
Speaker 3: speed ray line, and they are the two things that
Speaker 3: will create problems. We have enough stories and you know,
Speaker 3: you know the story about the high speed ray line
Speaker 3: in China where the trains derailed and got a substantial
Speaker 3: number of people were killed and top civil servants were
Speaker 3: jumping out windows and got one person got a death
Speaker 3: sentence and so on. So it's not like high speed rating.
Speaker 3: It's been non problematic in the China with just the
Speaker 3: evidence that we have, and based on the evidence that
Speaker 3: I have from conventional rail in China, I would be
Speaker 3: surprised if high speed rail in China was a walk
Speaker 3: in the park. I don't think that's the case, but
Speaker 3: we'd have to get the data in order to give
Speaker 3: a final verdict.
Speaker 1: Done that, Yeah, I think that they did well China,
Speaker 1: being China being very engineering and STEM center can very
Speaker 1: economically focused.
Speaker 3: I would say they cut off some of the fat.
Speaker 1: Tale simply besays they unlike Europe, where high speed rail
Speaker 1: runs on different gauges in every country practically, and in
Speaker 1: the UK, where I believe high speed rail runs on
Speaker 1: acting different gages and regular rail et cetera, et cetera,
Speaker 1: et cetera. China standardized a bunch of that stuff. And
Speaker 1: if you look at some of their big high speed
Speaker 1: rail stations, like in Beijing, every train is the same
Speaker 1: as every other train, you know, and you talk about that,
Speaker 1: but repent repetition, you know. And if we look at
Speaker 1: you have an example in the book which another subject
Speaker 1: unfascinated by, which is subways in urban centers. Same problem
Speaker 1: right North America and the Western world. The subways typically
Speaker 1: are really expensive. And elam Berteau actually does. I feel
Speaker 1: like Elan Berteau must be an honorary urban geographer. Do
Speaker 1: you know Alan Berteu in his work?
Speaker 3: I know the name, but I might have come across
Speaker 3: the work. There's so many people and so much, so
Speaker 3: many great people exactly. So Berteau's point was he was
Speaker 3: the chief urban planner for the World Bank for like
Speaker 3: fifty years, and so he went into China when it
Speaker 3: was going through the economic transformation to assist them with
Speaker 3: urban planning that made more sense than was there.
Speaker 1: He went into the Soviet Union or with Russia after
Speaker 1: the collapse of the Berlin Wall, to assist them to
Speaker 1: try and figure out what to do with the Stalinist
Speaker 1: apartment blocks and the city development. And he went into
Speaker 1: African countries with no cities and laid out roadge grids.
Speaker 1: And his perspective on subways was interesting. He said, the
Speaker 1: subways in New York are just as the price per
Speaker 1: square meter for land is the same as the land
Speaker 1: directly above them. So it was a kind of an
Speaker 1: interesting urban geography point. Yeah, you also counter with a
Speaker 1: specific example of a subway, an urban subway problem project
Speaker 1: which came in cheap. You know, I have seventy six stations.
Speaker 1: Can you tell us about that one?
Speaker 3: Yes, that's Madridge. But before we go to Madrid, let's
Speaker 3: just returned to China to round all discussion about the
Speaker 3: high speed rade in China. I would say that I
Speaker 3: would be very surprised given the volume of high speed
Speaker 3: rail that has been built rather than links, rather of
Speaker 3: a high speed rail that has been built in China,
Speaker 3: if there wasn't some positive learning, I would be very surprised.
Speaker 3: If we went in and started this and found there's
Speaker 3: no positive learning. That would be almost impossible building that
Speaker 3: much in such a short period of time. So I
Speaker 3: would expect that to be some of that, and that
Speaker 3: would support your argument. But the bottom line is, Mike,
Speaker 3: we don't know because we don't have the data from China,
Speaker 3: and this is too often the problem when we work
Speaker 3: with China. China is so important. As expert on megaprojects,
Speaker 3: I can tell you there's no other country that is
Speaker 3: more important, because there's no other country that is building
Speaker 3: more megaprojects than China is doing and have been doing
Speaker 3: for decades. So I would just love to get my
Speaker 3: hands on those data. I've been in China. I've talked
Speaker 3: to my Chinese tottage about that, and they say, forget it.
Speaker 3: You know that it's just up the way China works.
Speaker 3: If the central leadership says that this is what the
Speaker 3: data show, that's what the data show, and they don't
Speaker 3: want any researchers looking at what the data actually show,
Speaker 3: you know, if there's an official story about what the
Speaker 3: data show. So that's the problem. You know, work with
Speaker 3: the projects in China and many other things in China.
Speaker 3: This is not just for megaproducts. Economists who are studying
Speaker 3: the national economy have the same problem. Okay, let's China.
Speaker 3: Let's let's go to Spain now and to Madrid, because
Speaker 3: that's one of the places where we found a team
Speaker 3: that we're totally able to beat the odds in the casino.
Speaker 3: So the odds for urban rail are not that good.
Speaker 3: You know, you will have large cost overroadens on average
Speaker 3: in constant prices, you would have like forty cost overrun
Speaker 3: on average, you would have delays, you would have lots
Speaker 3: of passengers in the forecasts that never show up in reality.
Speaker 3: So that's that's your standard urban rail project on average.
Speaker 3: So urban rail actually follow the iron law over budgets
Speaker 3: over time, on the benefits, over and over again, except
Speaker 3: with fire. This example in Madrid, you know an outlier
Speaker 3: that were built twice as fast as urban rail is
Speaker 3: normally done to schedule and half the cost to budgets
Speaker 3: and basically got the passengers that they projected. Yeah, now,
Speaker 3: how on earth did they do that? You know? And
Speaker 3: they did it by doing what nobody thought could be done,
Speaker 3: modularizing on the ground rail like modularizing subway. So this
Speaker 3: team in Madrid. They figured out there's got to be
Speaker 3: an ideal link for a tunnel boring machine, you know,
Speaker 3: boring a tunnel for a subway. So they started measuring that,
Speaker 3: what's the ideal links for one tunnel boring machine with
Speaker 3: one team you know running that tunnel boring machine. They
Speaker 3: figured it out and then they you know, they were
Speaker 3: in the process of doing the largest expansion of a
Speaker 3: subway system in the world ever at the time. Now
Speaker 3: China has done more, but but this was this was
Speaker 3: outside China. Madrid's expansion was like much larger than usual,
Speaker 3: and they figured, we got to get this done. This
Speaker 3: this is the policy of Madrid. We need to get
Speaker 3: it done. And then they just hired as many tunnel
Speaker 3: boring machines as they needed for whatever length it was
Speaker 3: that they were they were building, you know, and they
Speaker 3: would actually get eight tunnel boring machines and tenes in
Speaker 3: on theer Madrid, you know, to work at one time,
Speaker 3: you know, when they had the most teams going, and
Speaker 3: instead of taking you know, eighten years a building an extension,
Speaker 3: they would take four years to build the extend. They
Speaker 3: would work around the clock, which is actually not common,
Speaker 3: you know, usually for a different reasons, like not to disturb,
Speaker 3: you know, not to have construction going on at night
Speaker 3: and on weekends there's a lot of downtime. And they
Speaker 3: negotiated with the local community groups that hey, we can
Speaker 3: take ten years to do this, so we can do
Speaker 3: it in four years. You know, if we work around
Speaker 3: the clock twenty four to seven, we can do it
Speaker 3: in four years. If we abide by the usual rule
Speaker 3: of not working at night and not working on weekends,
Speaker 3: it'll take more than twice as long. What do you prefer?
Speaker 3: And they didn't hide that. They preferred to do it,
Speaker 3: you know, twenty four to seven, and they got that
Speaker 3: through the community groups actually accepted that. So that's one thing,
Speaker 3: you know, very good collaboration with local community groups. And
Speaker 3: then this thing about modularizing the different parts of the
Speaker 3: metro and also stations. They just you know, in many
Speaker 3: metros around the world, you know, in London, in Moscow
Speaker 3: and so on, you will find that the stations are
Speaker 3: almost like pieces of arts and each station is different,
Speaker 3: and you get fancy architects to design the stations. In
Speaker 3: Madrid that decided, no way, we're not going to invite
Speaker 3: signature architecture. So signature architecture is one of the other
Speaker 3: areas that typically has very large cost overruns and delays
Speaker 3: and so on. And they said, in Madrid, why we
Speaker 3: would be so stupid that we would invite that kind
Speaker 3: of economic risk in by having specially designed stations by
Speaker 3: famous architects. We'll do the exact opposite. We'll make a
Speaker 3: very very nice, big area station. We're not going to
Speaker 3: drill it. We're going to do cut and cover. So
Speaker 3: we just take a big take a big hole, and
Speaker 3: we put in the station and recover it and that's it,
Speaker 3: and we'll do We'll do the same station pretty much,
Speaker 3: you know, around Madrid, so that we get positive learning
Speaker 3: curves every time we do a station, we do it
Speaker 3: better next time, and better after that and so on,
Speaker 3: as opposed to if you do the spoke stations that
Speaker 3: are each designed by what a famous architect, each one
Speaker 3: will be different and you won't be able to get
Speaker 3: these positive learning curves. So they really maxed out on
Speaker 3: all these things. They also decided no lawsuits. And you
Speaker 3: know in construction lawsuits are so common. You know, this
Speaker 3: is the actually you write the wrong contracts up front,
Speaker 3: contracts that actually encourage conflicts you know that people start
Speaker 3: thinking about how can we sue each other when things
Speaker 3: go wrong? From day one, you know, this is the
Speaker 3: first thing, but even before day one, this is what
Speaker 3: they think about when they design the contracts. And if
Speaker 3: you design your contracts like that, when things go wrong,
Speaker 3: and they always do, there's always something that goes wrong
Speaker 3: on products of the size that we're talking about here,
Speaker 3: then people start suing each other. In Madrid, they decided
Speaker 3: we're not going to write our contracts like that. We're
Speaker 3: actually going we the client, are going to take on
Speaker 3: a lot of the construction risk. We're not going to
Speaker 3: try to allocate this to the contractors because we've tried
Speaker 3: that and it usually doesn't work. Even if we thought
Speaker 3: we had signed it over to the contract that it
Speaker 3: always somehow mysteriously inpact with us, you know, So why
Speaker 3: don't we just face that back and then accept that's
Speaker 3: the way it is, and then we get a partnership
Speaker 3: with our contractors where we collaborate on getting as usos
Speaker 3: risk to materialized as possible and we pay the contractors
Speaker 3: to avoid it instead of suing each other, you know,
Speaker 3: when things have gone wrong. So there was like a
Speaker 3: handful of basic things like that they didn't Madrid that
Speaker 3: worked out when you put them all together, it worked
Speaker 3: out beautifully, delivering at half the price, twice as far
Speaker 3: and very functional subway. If you've been there and you
Speaker 3: try that, you know that this is acting a system
Speaker 3: that really works. It's very large for a city of
Speaker 3: the size of Madrid. They have a fantastic you know,
Speaker 3: mitro system.
Speaker 1: Oh, this gets into I want to dig into thinking
Speaker 1: slow and acting fast because when you talk about acting
Speaker 1: when you talk about the duration of megaprojects. My supposition,
Speaker 1: I don't think it was crisply laid out in the
Speaker 1: book because I think you assume that it's just so
Speaker 1: internal to you. But I think when you talk about
Speaker 1: acting fast, you're talking about the delivery phase where construction,
Speaker 1: you know, after the shovel hits the ground until completion.
Speaker 1: So the duration, the duration you're using is for that portion.
Speaker 1: So for Madrid, it's when the first construction site was had,
Speaker 1: the first shovel in the ground is to start. But
Speaker 1: that thinking slow process is intentionally and rightly excluded from it. Now,
Speaker 1: can you characterize thinking slow versus acting fast because it's
Speaker 1: such a fundamental to a premise in your book.
Speaker 3: Yeah, yeah, that's that's a key premise one of the chapters.
Speaker 3: It's called that you know. And if we take the
Speaker 3: Madrid example again, then the thinking slow is the leadership
Speaker 3: in Madrid actually thinking up all these rules of some
Speaker 3: that I just mentioned. You know, we have to have
Speaker 3: good stakeholder management with the community groups. We're not going
Speaker 3: to sue anybody. We're going to modularize tunnels intersegments of
Speaker 3: optimal lengths in relation to what it's very relevant to
Speaker 3: actually what you talk about, Mike, it's about the basic
Speaker 3: what are the basic physics of this and and and
Speaker 3: getting back to the basics. So what's the optimal links
Speaker 3: that one tunnel machine will do, and then we'll just
Speaker 3: hire as many tunnel boring machines that we need to
Speaker 3: do the total links that we need to do right
Speaker 3: and the same with the station. So that's the thinking slow,
Speaker 3: thinking all this through before you do anything, instead of
Speaker 3: what usually happens is that people only figure out these things,
Speaker 3: you know, while they're delivering, you know, while construction is
Speaker 3: going on in Madrid that did it before, and that's
Speaker 3: what we find. This is what intelligent intelligence master buildings
Speaker 3: are doing and that's that's what we call the people
Speaker 3: who do it this way, we call it master that
Speaker 3: they're really mastering what they're doing, and they are mastering
Speaker 3: it by what masters always do is like they really
Speaker 3: think things out, you know. But then once they get going,
Speaker 3: they know the clock is ticking. And this is the
Speaker 3: reason that it's so important to egg fast. Once you've
Speaker 3: got the shoveling in the ground, as you say, and
Speaker 3: for an it project, of course it won't be a shovel,
Speaker 3: it'll be something different. But once you start delivering, you
Speaker 3: need to go fast because that's how you reduce your risk.
Speaker 3: We call it the window of the doom in the book.
Speaker 3: So there's a window and that's the time window from
Speaker 3: you start delivering until you finish delivery. That's the window.
Speaker 3: And that's a window of doom in the sense that
Speaker 3: that's where you can really get hurted. You and your
Speaker 3: project can really become expensive. It is when you so
Speaker 3: if your tunnel boring machine is flooded, like what happened
Speaker 3: on on the high speed rail project in Hong Kong
Speaker 3: that you talked about, and on a tunnel in Denmark
Speaker 3: that we also talk about. It's actually surprisingly in common
Speaker 3: you know that you have tonnel boring machines that get flotted,
Speaker 3: and these machines are expensive, very difficult repair because they're
Speaker 3: in a hole on theerground, right, So they create shoes
Speaker 3: delay if you get problems like this. But that's the
Speaker 3: kind of thing, and that's why we call it the
Speaker 3: window or doom. All these things can happen in that window.
Speaker 3: Like obviously, you want that window to be as small
Speaker 3: as possible. You particularly want it to be so small
Speaker 3: that no fat black swan can fly through it, you know,
Speaker 3: and mess up your project. And the smaller you make it,
Speaker 3: the less risks you have of these things of any
Speaker 3: type of risk, including black swan risk. So that's the
Speaker 3: reason why projects that are able to move fast in
Speaker 3: delivery have much smaller risks than projects that take all
Speaker 3: the time. A lot of people don't think of it,
Speaker 3: you know, like there we have lots of time. It's
Speaker 3: not a troplet. We take ten years to deliver a project. Well,
Speaker 3: let me tell you it is a problem. If you
Speaker 3: take ten to fifteen years to deliver a megaproject, which
Speaker 3: is not uncommon, you can count on that something really
Speaker 3: bad is going to happen during that period, just because
Speaker 3: that's the nature of things, you know, that's history. You know,
Speaker 3: you would have a major financial crisis, you'd even have
Speaker 3: a pandemic as we've seen now everybody nobody has been
Speaker 3: thinking about pandemics saw, you know, eighty ninety years, because
Speaker 3: we haven't had one for about one hundred years.
Speaker 1: Right, really bad one that our public health surreil and system.
Speaker 1: And as a you may have noticed, I did actually
Speaker 1: help build the world's most sophisticated, outbreaking communical disease managements
Speaker 1: STEAM in the world after SARS. And since SARS we've
Speaker 1: had h one n one, we've had a bowl up. Yeah,
Speaker 1: and now this what I articulate is we have these
Speaker 1: amazing resilience built in because we've mostly learned our lessons.
Speaker 1: We keep forgetting because climate change and pandemics are gray swans. Yeah,
Speaker 1: they're gray rhinos. They're not black swans. They are expected.
Speaker 1: But I will say, let's just take the duration. The
Speaker 1: median duration in decades is it takes ten years to
Speaker 1: construct a nuclear power plant, and so much stuff is
Speaker 1: happening so quickly. A decade ago, it was possible to
Speaker 1: look at the data and say, we don't know if
Speaker 1: wind and solar will be viable. We don't have good
Speaker 1: data on grid integration, We don't have good data on
Speaker 1: how they'll integrate with markets, We don't have good data
Speaker 1: on grid reliability with significant portions of that, and they're
Speaker 1: still fairly expensive. But any nuclear reactor started a decade
Speaker 1: ago in construction, it went into that coming into market
Speaker 1: today is facing a radically different market competitive situation because
Speaker 1: wind and solar have proven themselves grid reliable, cheap, stable,
Speaker 1: and are now starting to take over incillery services on
Speaker 1: grids as well, which is really interesting but really nerdy
Speaker 1: and output that aside. Kind of the point is that's
Speaker 1: that window of doom. The more you can shrink that,
Speaker 1: the more likely that your business case assumptions for some
Speaker 1: thing are still going to be valid when it goes
Speaker 1: into production, and things like the Ukraine War for example,
Speaker 1: don't impact what's going on. And you can see that,
Speaker 1: you know.
Speaker 3: Yeah, you know, you know. Fild tedlock Ye whose co
Speaker 3: author with Dan, my co author on the new book
Speaker 3: on the book called super Forecasting. So Dan wrote on
Speaker 3: filter Ted called super Forecasting before he wrote this book
Speaker 3: with me called How Big Things Get Done. And he
Speaker 3: has a law that I call titlock y'all, and that is,
Speaker 3: you know, you have a certain reliability of your forecast
Speaker 3: the first two or three years of the forecast, and
Speaker 3: after three to five years you can pretty much forget
Speaker 3: any certainty at all of your forecast. So that tells
Speaker 3: you everything. That means that you actually need to have
Speaker 3: a substantial parts, maybe the major part of your project
Speaker 3: needs to be done within two to three years. You
Speaker 3: should have as little as possible beyond three five years
Speaker 3: because that's the completely uncertain part and that's where the
Speaker 3: window of doom will bite you. So that's that's why, Yeah,
Speaker 3: that window needs to be kept real small. And we
Speaker 3: can see it in the data. It's very clear this
Speaker 3: is not something this is not speculation. This is something
Speaker 3: that we can see it in the data. That the
Speaker 3: face that you are, the lower the risk you get.
Speaker 3: And that's you actually win on two fronts. You you
Speaker 3: you reduce risks generally, and you even more dramatically, you
Speaker 3: reduce black lawn risk.
Speaker 1: Well, there's another thing that I'd like call out that
Speaker 1: you you articulate clearly against the Tesla gigafactory and against
Speaker 1: wind and solar farms, which is that modularity enables you
Speaker 1: to start accruing benefits before you've completed everything, you know,
Speaker 1: so that one's the point where you want to win.
Speaker 1: When a wind farm is in, what ten percent of
Speaker 1: is in it can be generated in electricity all the
Speaker 1: other ninety percent of the wind farm is completed. The
Speaker 1: lord for solar farms similar intesslic Do you want to
Speaker 1: speak more to that, because I feel that is under
Speaker 1: under estimated as a shrinking of the window of doom.
Speaker 3: I think that the whole, the whole discussion of benefits
Speaker 3: is hugely under discussed and underestimated. They're much more important
Speaker 3: than we think. You know that to get to the
Speaker 3: benefits is really important. And the reason that became so
Speaker 3: clear with Tesla was that at that time Musk was
Speaker 3: not the rich guy that he is now. He was
Speaker 3: actually in shoot Steads. And you know, so when he
Speaker 3: was building his first gigafactory, which was called Gega Factory
Speaker 3: one at the time and it's still called Giga Nevada
Speaker 3: because it's the Giga factory in Nevada, and he heard
Speaker 3: that it would take five years, you know, for the
Speaker 3: normal construction industry to build a factory like that, he said,
Speaker 3: no way, I mean, if I have to wait for
Speaker 3: my revenue stream for five years, I'm dead. You know,
Speaker 3: test that is not going to exist if I have
Speaker 3: to wait for five years to get to my revenue stream.
Speaker 3: And he said no, and he didn't talk about, as
Speaker 3: far as I know, talk about Titlock's law, but he
Speaker 3: acted as if he understood this law. We need to
Speaker 3: be in business and generate revenues within the first year,
Speaker 3: the first two or three years, the first year. So
Speaker 3: he designed the factory. He said, like, let's let's not
Speaker 3: even talk to these guys at conventional instructions. And I
Speaker 3: know this for a fact because people from I know,
Speaker 3: people from Convention Construction who tried to call Mosque and
Speaker 3: get a dialogue with him about building the first Geka factory,
Speaker 3: and he would not talk to them. He said, we're
Speaker 3: going to reinvent this ourselves. Not a lot of people
Speaker 3: know this, but this is actually the secret source of
Speaker 3: a lot of what Mosk is doing. Is that he
Speaker 3: rethinks things to the basics and their modularity is actually
Speaker 3: or standardization is a key to that, both for Testnet
Speaker 3: but also for spasics and allus. You can you can
Speaker 3: see it if you start looking at what he's doing
Speaker 3: and including the Geka factory. So he actually decided on
Speaker 3: a design where the Geka factory was consist of twenty
Speaker 3: one modules, where each module could function as a factory
Speaker 3: in its own rights. And then you know, so you
Speaker 3: just build one of the twenty one modules. And they
Speaker 3: did that within the first year there a bit sever
Speaker 3: within the first year, and they were immediately in business.
Speaker 3: They were producing batteries and the famous that what is
Speaker 3: now called the power wall was coming out of there
Speaker 3: within one year, and they had a revenue stream that
Speaker 3: cloud back into Tesla, and Andy financed that growth. And
Speaker 3: then they would build another module and that would be,
Speaker 3: you know, combined with the first, and now they would
Speaker 3: have a larger piece of factory and so on and
Speaker 3: so forth. And that's how they that's how it scaled
Speaker 3: up the factory. And they also had positive learning to
Speaker 3: the degree that they realized, we actually don't need as
Speaker 3: many modules as we thought we did, you know, because
Speaker 3: we're getting more and more efficient the more we do this,
Speaker 3: So we can now produce more volume of batteries in
Speaker 3: thrower modules of factory. So they got these kinds of
Speaker 3: efficiencies through the positive learning curves. So that's that's a
Speaker 3: that's a clear story about how modularity can work positively
Speaker 3: for you. And yeah, we included that in the book.
Speaker 1: Well and and for you know, on my primary concern,
Speaker 1: which is at electrical generation, though I dot all with
Speaker 1: transportation as well for the question of wind, solar and nuclear.
Speaker 1: As soon as you've got the transmission link in and
Speaker 1: you're putting in your first wind pat wind far wind
Speaker 1: turbines or solar panels, you can actually start feeding electricity
Speaker 1: to the market. But with nuclear you have to be
Speaker 1: all the way to the end and say a whole
Speaker 1: bunch of regulatory approule and you turn.
Speaker 3: It on on one day. The gigawat gets.
Speaker 1: Turned on in one day at ten years out and
Speaker 1: all the debt and revenue has foregone, and it's just
Speaker 1: that problem until then. Whereas you know, with the modularity solution,
Speaker 1: that's part of the reason it's so you know, advantageous.
Speaker 1: I think the takeaway for institutional investors, policy makers, energy
Speaker 1: strategists is they should look at that chart in your book.
Speaker 1: I'm not going to say what page it is for
Speaker 1: the simple reason that I read it on kindle and
Speaker 1: page numbers are wonky on kindle, But it's which chapter
Speaker 1: is that amazing chart of variants? And I think's the
Speaker 1: last chick.
Speaker 3: So that's actually I don't have the final book yet,
Speaker 3: can you believe it. It just came off the press,
Speaker 3: So I don't even know if the page numbers are
Speaker 3: going to be the same, but I think they are,
Speaker 3: so letd me just find that job for you. So
Speaker 3: that's the job with the variants. It's on page one
Speaker 3: seventy three and it's the final chapter card What's your Lego?
Speaker 3: That's chapter nine. So chapter nine called What's your Lego?
Speaker 3: Page one hundred and seventy three, there's a diagram with
Speaker 3: the variance on different projects.
Speaker 1: Yeah, I think this is such an important part of
Speaker 1: this and as you say, this is the first time
Speaker 1: you published it. I think every policymaker, strategists, institutional investor
Speaker 1: could buy this book for that charge in that chapter,
Speaker 1: and then they should look at that and say, what
Speaker 1: is my risk profile in my portfolio of major infrastructure
Speaker 1: projects based on nets and what can I do about
Speaker 1: it besides call up Ben Flusberg and his firm to
Speaker 1: help me figure out how to modularize this and avoid stuff.
Speaker 3: Yeah, it's because.
Speaker 1: You're a small you're you're you're as a buddy of
Speaker 1: mine says, you've created frameworks and you help sell people
Speaker 1: ladders to help them with their problem. But there's only
Speaker 1: so many ladders you can help people with.
Speaker 3: Yeah, we talked, we we we we we do talk
Speaker 3: to institutional investors from time to time, including pension funds.
Speaker 3: So pension funds in Benma, my home country, are big,
Speaker 3: you know, investing in in infrastructure including you know, energy infrastructure,
Speaker 3: wind farms and so on. And they're beginning to get it.
Speaker 3: But it's actually taken a while, you know that, Like that,
Speaker 3: like the thinking and the financial six is unfortunately so conventional,
Speaker 3: and they have all had the same statistics. One O
Speaker 3: one course is that they don't understand extreme value theory.
Speaker 3: This is what Mesimtelip has been pointing out all the time,
Speaker 3: you know, like he's been he's really been pounding this
Speaker 3: message that that this is the problem. And and I
Speaker 3: can say that my experience confirms that it is a problem.
Speaker 3: But I do think that we be beginning to get
Speaker 3: at rode through and we are trying to explain that
Speaker 3: the risk that you're looking at, I'm not the relevant
Speaker 3: risks you know, is completely different risks, and those are
Speaker 3: the risks that we try to highlight with this diagram.
Speaker 1: Yeah, so I have to say, amazing book. It resonated
Speaker 1: so strong with me because I've worked on billion dollar
Speaker 1: it projects and I've fixed certain numbers of them or
Speaker 1: tried to, and I killed a few, you know, as
Speaker 1: a troubled project fix it guy, and I launched a bunch.
Speaker 1: But I think that what I was expecting more from
Speaker 1: the book was more the Code of the Heuristics, because
Speaker 1: that seems like if so much of your publication is
Speaker 1: about those types of heuristics, and so I recommend for
Speaker 1: people who finished chapter eight to keep reading. The Code
Speaker 1: of Heuristics is a very useful set of stuff to
Speaker 1: paste on a wall to remind yourself as you plan
Speaker 1: and think about projects and delivered projects. Don't screw up.
Speaker 1: They're very useful. Now I'm going to be respect I
Speaker 1: have to be respectful of your time. I know you
Speaker 1: know you and you're a firmer, are very busy in demand,
Speaker 1: and your cycle of interviews should be increasing radically as
Speaker 1: this book comes out. So I always like to leave
Speaker 1: an open ended opportunity. We you know, clean tech talks.
Speaker 1: We've got about a fifty percent US audience and about
Speaker 1: a fifty percent global audience. We've been talking about the transformation,
Speaker 1: we've been talking about climate Chaine and talking about risks.
Speaker 1: But what if you had like just an open ended
Speaker 1: opportunity to give guidance to people based upon your perspective.
Speaker 3: What would it be? If I could say only one thing,
Speaker 3: it would be understand your base rates. And base rates
Speaker 3: are like your basic risks. Like what we talked about
Speaker 3: people going to the conceine, the cansino, the base rates
Speaker 3: and again a casino are the odds you know, in
Speaker 3: the casino for the individual game. So there's a base
Speaker 3: rate for playing the lids, there's a base rate for
Speaker 3: playing blackjack, and so on. I find that most people,
Speaker 3: both doing projects and investing in projects don't understand what
Speaker 3: the base rates are. And that fits complete with behavioral economics.
Speaker 3: So this is there's something called the base rate fallacy.
Speaker 3: That's our fallacy. We are hardwired not to get the
Speaker 3: base rate base rates rights. And that's actually the most
Speaker 3: simple thing we can do is to get the base
Speaker 3: rates right. And we know how to do this now,
Speaker 3: like we have the data for this. We know how
Speaker 3: to do it with reference cards, forecasting and so on.
Speaker 3: So that would be my first thing, but there's many things.
Speaker 3: And even though you mentioned that the heuristics are in
Speaker 3: the in the code, there are eleven heuristics, to be specific,
Speaker 3: and I agree, I really encourage people to get there,
Speaker 3: but we have also spread them out through the book.
Speaker 3: You know, they pop up in different places in the book,
Speaker 3: also in context with specific examples, you know, and with
Speaker 3: specific people actually using them and being successful using them.
Speaker 3: So that's another thing I would I would say, in
Speaker 3: addition to getting your base rates right, I would say
Speaker 3: get your heuristics right. Start working on your heuristics. This
Speaker 3: is an individual thing. Each master builder has his or
Speaker 3: her own setup heuristics and and I haven't met a
Speaker 3: master ability that does not have heuristics, you know, So
Speaker 3: that's another thing. If you don't, if you haven't worked
Speaker 3: on your heuristics, start thinking about this, and it might
Speaker 3: be a good place to start. In the coda there,
Speaker 3: it's very just a few pages, and as we say there,
Speaker 3: we put those heuristics in there to inspire your heuristics,
Speaker 3: so you can see which one do you o resonate
Speaker 3: with and which one would you change, and you probably
Speaker 3: have additional heuristics that you would add to that list.
Speaker 3: That's another thing to do.
Speaker 1: Thank you very much. So I'm Michael Bernard and missus
Speaker 1: Clean Tech Talks, and my guest today is being Bent Filiberg,
Speaker 1: the first BT Professor of the Science School of Economic
Speaker 1: Socksford University. He also has a professorship at the IT
Speaker 1: University of Copenhagen, but those things are an aid of
Speaker 1: him being the world's leading megaproject expert. He consults globally,
Speaker 1: he assists people. His intellectual capital on how to manage
Speaker 1: risk and programs is used globally and it's very applicable
Speaker 1: and very good news for clean technology and the transformation
Speaker 1: we have to do. Ben, thank you so much for
Speaker 1: your time today. Really appreciate speaking with you, and I
Speaker 1: wish I had six more hours.
Speaker 3: Thank you likewise, Mike, and thank you for giving me
Speaker 3: this opportunity to talk about these things. Thank you so much.
Speaker 1: This is Mike Bernard, host of Redefining Energy Tech. My
Speaker 1: guest today has been Professor Benflobier.
Speaker 3: If you don't have.
Speaker 1: His book How Big Things Get Done on your desk,
Speaker 1: get it now. Thank you for listening to Redefining Energy,
Speaker 1: don't forget to rate the show and subscribe on Apple Podcast, Spotify,
Speaker 1: or the platform of your choice.