Innovation: Coming up with new ideas

Hey everyone, it's Russell and Christine. So I just found this mobile game everyone's talking about. Royal Match. Gorgeous graphics and super fun puzzles.

Bro, you're late.

I'm already at level 700. I play it every day on the subway because it doesn't need wi fi.

Wait, what? I've got to catch up.

Oh, and they just added new minigames. They make it even more fun and challenging.

Alright, show's over. I'm gonna go play.

Download Royal Match on the App Store or Google Play. Today.

The Economist.

Tell us what we're looking at.

You're basically seeing the robots building and our robots.

I'm on a building site and wearing a hard hat. I've never felt more handy. I'm here because Salah Al Faji is showing me his vision of the future. Alfaji isn't a builder, he's not an architect. He's the co founder of a Dutch startup called Monumental. It makes AI driven bricklaying robots and they work together.

They're coordinating like one is laying mortar and then the other one is pushing down the brick in there and they grab the brick from another robot, which is the brick supply one, and that one can just drive off, grab more bricks, come back.

Teams of four wheeled robots are working alongside human masons on a new residential property development about 25 minutes from Rotterdam. The house is probably two stories high.

Two story house.

Yes, yes.

See, I'm using words like story as though I'm like, you've been in this for ages. A supervisor from Monumental, who has a bit more building experience than me and also knows how to count, is on hand to supervise the robots and intervene if needed. But the supervisor doesn't control the robots, they act autonomously. So the arm with the mortar is now positioned above the brick and it is going to stop. One robot squeezes mortar onto a brick. Kind of looks vaguely disgusting. Alright, it's running along the length of the brick. Okay. Then the arm moves back. Now another picks up the brick and places it onto the beginnings of a wall. The other arm, which is holding a brick, comes and places it on top. Placing a brick is not as simple as it sounds. Camera vision and a software representation of the wall ensures that it goes in precisely the right place. The robots have also copied a trick from human masons, a specific way of increasing the adhesive connection between brick and mortar.

So what it's actually doing is it's actually like pushing and vibrating into the mortar. There are sensors into the end effector because it's actually. You don't like place a brick on the mortar, you actually need to push it down. And what mesas do, we discovered, is actually vibrate their hands a bit.

Monumental was founded to solve a real world problem, a shortage of bricklayers. There's a long way to go before its robots can fill that gap. They can't build interior walls, they can't do soldier coursing, which as everyone knows, is a row of bricks laid standing up with the narrow edge facing out. They hit problems, things go wrong. Like bricks being dropped mid task.

That dropping worries me, actually.

You've seen that before.

I've not seen this before. Not like this.

It's just like people.

Journalists come in, everything goes wrong.

Disaster.

Str.

Bloody journalists. Despite the mishaps, Dutch families will soon live in houses part built by machines. Even seeing a few bricks being laid in a kind of robotic choreography is hypnotic. This must make you feel.

Yeah, of course.

It's amazing.

Like when you have a streak of this for like an hour or two, it's just like magical.

Yeah. If you were asked to conjure up an image of innovation, what do you picture? Hammocks, ping pong tables and whiteboards. People saying there's no such thing as a bad idea. Despite the existence of Hawaiian pizza and golf, Salah Al Faji, whose robots really are at the cutting edge, has little patience with all that nonsense.

Just like culturally, we have a very strong aversion towards, like, I just call it like fake innovation, which is just like colored post it notes beanbags. There's this kind of aesthetic of innovation which is not innovation.

I'm Andrew Palmer, the management columnist for the Economist. The performances and practicalities of the workplace are what I write about each week. In the first season of this podcast on how to be a good manager, I learned how to hire people, how to run teams, and how to motivate employees. I also discovered how important great bosses are and how rare. Managing well is extremely difficult, but it is a skill that can be learned. So I'm back with a new set of questions for the bosses of today and tomorrow. You'll hear tips from Levi's, Novo Nordisk supercell and more of the world's best performing companies on how to negotiate, how to present, how to take decisions and more. In this episode, we'll look at the holy grail of the innovation. Every manager has been asked to brainstorm product ide job candidates boast about their ability to ideate. The one thing you know about AI is that it's going to mean doing new things in New ways. So to find out what proper innovation looks like. The reality of it, not the aesthetics. We'll meet three very different firms. Wave is a deep tech company trying to make self driving cars a mass market reality. Lego is an established industry leader and an innovator at scale. Google is threatened by disruption. It faces the challenge of overhauling its flagship product to make the most of AI. We'll learn that innovation does not involve throwing a word association ball to your colleagues and wishing you were dead. It's more like the building sites where Monumental is training its robots. People know the overall goal, but the process is often messy. The pathway is frequently unclear. From the Economist, this is Boss Class Season 2 on this episode, the ingredients of innovation.

I think the biggest bullshit is Eureka. Ideas where you just wake up and have an idea that solves things. Breakthroughs are achieved by a sustained effort over many, many years.

Alex Kendall is the founder of Wave. It's a startup which makes AI software for self driving cars.

And it's a single AI model that can learn to drive these different vehicles in different environments, safely around all kinds of scenarios, from urban to highway driving, to sun to rain, and even some examples in the snow.

Like Monumental, Wave is an embodied AI company. But it's much further along in its journey. In 2024, Wave raised over $1 billion in funding, the largest ever investment in a European AI firm. Its tech is now being adopted by some very big names. The Japanese car maker Nissan has announced that by 2027 its system will use Wave's self driving software. For a long time, however, Wave's approach made it an outlier. Other self driving firms were writing rules for what a car should do in specific circumstances. When the middle lane is empty, hog it, that kind of thing.

The largely Chinese and Silicon Valley esque efforts were focusing on creating the infrastructure and hand engineering the rules to be able to make these systems behave. And that produced incredible pilots and demonstrations. But I just was convinced that that wouldn't be the way that this technology would be able to scale globally. It's not a classical robotics problem, it's not an infrastructure problem, but it's an AI problem.

If you're wondering why it's an AI problem, here's the explanation I think about.

Every time we go driving, the scenarios are different, the weather's different. I was driving yesterday and a lollipop person came out on the pedestrian crossing.

For those across the Atlantic, a lollipop person is a crossing guard. In Britain, they're required by law to carry an enormous lollipop which they lick whenever someone crosses of them and was.

Guiding the traffic using hand gestures and things like this, or just scenarios that are very, very hard to tell the car what factors to look for. But the beauty of embodied AI is that it can learn patterns in the data that are more complex than we can hand engineer. The beauty is that it can scale very efficiently.

The idea of a self driving system capable of making its own decisions has become much more orthodox now. But when Kendall first formed this conviction as a PhD student at Cambridge University eight years ago, it was audacious.

Finishing my PhD, I went and spoke with many automotive companies, big tech, self driving companies, and I was given feedback that this would never work. It's unsafe. Why don't you just use it for perception technology? I certainly had the conviction there that no, this would be how things work. This is where I want to devote my time. And look, it's certainly not been a linear or exponential growth towards this point. There's been a lot of ups and downs and a lot of points where any rational person would have looked at this and said, this is a fool's errand.

Working on a breakthrough technology like this requires a mixture of optimism, bloody mindedness and flexibility. Kendall likes to talk about tackling the hardest problems first to minimize the risk of hitting a roadblock later. That explains one of the biggest early decisions he made, to move Wave from Cambridge to London.

We didn't go to a desert city where the sun always shines. You've got clean weather, you've got wide boulevard roads. But we went to this medieval city of transport network with lots of merging and I mean, we've quantified this. It's got seven times more cyclists and jaywalkers than a city like San Francisco. Of course the weather is varied and it's one of the hardest places to drive in the world.

Good to know. Britain still leads the world in some things.

Starting there forced us to build scalable technology and also doing so with constraints like just a single GPU on the car and cameras. Not relying on all of this infrastructure and that forcing function forced us to build technology that could innovate and ultimately be more efficient, perhaps by orders of magnitude compared to our competition.

If Wave's vision has been consistent, its path has been adaptable. Technologies change, thinking develops. Three years ago, a few of Kendall's team told him they thought it would be worth investing in language models for robotics to improve the car's efficiency.

This was before the world had woken up to the power of language models. And I thought it was Absurd. I was at the time looking to keep the company focused and I was like, no, no, no, no, let's stay on task here.

The team persisted and they convinced me.

That language would be a key part of the future of robotics for a number of reasons. If you think about the promise of embodied AI, it goes beyond a robo taxi service where you're sitting in the car and it's like you're on invisible railway tracks and you just get in and go. But actually, we can build a system that's a chauffeur experience that can interact with you, ask you how you're doing, what kind of driving you'd like.

Schoolboy racer, please. With a donut at the end.

Language gives you a great ability to introspect the model and understand how it's thinking. You can ask it for feedback or ask, why is it doing a certain decision and build a sense of trust with it, or delegate a task that maybe it wasn't originally going to do and ask it to behave in a new way.

Kendall trusted that his team saw something he couldn't and Wave incorporated language into its models. The idea that a single system will be able to cope with whatever is thrown at it was given a boost last year when the company's tech was tested on American roads for the first time.

It was incredible. On the very first day we were driving in the U.S. i was there, I jumped in the car, going to San Francisco and we saw the car learn these new behaviors. So in the uk, of course, we don't have four way stop signs. You can't turn right at a red light. We drive on the left hand side of the road. In the US it learned all those behaviors so quickly and was able to demonstrate those on the very first day of testing. I mean, that was remarkable for me.

Wave has taught me a couple of things about innovation. One is the importance of a clear mission. Alex Kendall believed in his idea and stuck to it. The other is that conviction shouldn't lead to rigidity. Innovation is not linear. It zigs and zags. This blend of fixed points and flexibility leads us on to our next company. And like Monumental, it's also in the business of bricks.

So there's this lever on the back that you just fire the car. It's so fun.

You just smashed this thing and this Formula one car came across, hit our mic. That's good. One of Dan Meehan's latest creations has just collided with our recording equipment. All in the name of play. Meehan is a designer for Lego and responsible for its Formula one and Space Rangers. He is exceptionally, unashamedly enthusiastic about Lego, whatever form it comes in.

We made a handful of bricks. It was 12 or 14 of these bricks out of a 4.5 billion year old meteorite. For me it was just a wonderful project because I felt like a child again.

Feeling like a child again is pretty much the mission statement at lego. I've come to the toymakers headquarters in Billund in Denmark to learn about how they create must have toys for kids and big kids. When I met him, Mian was already working on products slated for launch in a couple of years time. I asked to see them but wasn't allowed to or even to visit the floor where the designers worked. New products are kept tightly under wraps. LEGO no go. The products he did show me had launched or were just about to. Meehan talked me through the design process for the space brief. It started with research into what drives kids passion for space. To find out, he interviewed children across five countries, including America, China and Germany.

And it was actually really interesting to find out that a lot of our preconceived notions of, yeah, kids want X actually they were thinking very differently about space, particularly from their parents. So they were thinking much more about it being about beauty and wonder and imagination and less about the traditional maybe Apollo era generation of striving to be an astronaut.

Meehan tries to understand what makes kids tickets. He speaks to their families too. They're often the ones paying. After all. Once he's done his research, it's back to the leadership team with some proposals.

And then they say yes or no, depending on how well we deliver the information and the story.

And then the designers start designing, sketching and visiting LEGO's vault of old products for inspiration.

So it's just something to get an idea out of your head, thinking, I think it needs to be about this big or I think it needs to be this colour. And then there's a process of refinement and you know, taking that through peer review.

The real test of a new toy comes when it gets into the hands of users.

The very best part of my job is when we do kids tests because kids are so honest and they will break the model instantly.

Kids will also use models differently than the designers expected.

There was a six wheeled space rover and for the whole of that play test, that vehicle with six big wheels and they had suspension and it didn't drive for the whole playtest it flew. So the young boy that picked it up, he just flew this thing around the room, collecting little Aliens and putting them in the back of the space truck. We asked about the six wheel vehicle because there was a lot of time going into the mechanism because it had full suspension and it had six big wheels, you know, which could we take those elements and change them for something? And we said, do we need to add, like, the boosters? Do we need to put bigger wings on it? He's like, no, it's cool just as it is, and it flies. I was like, okay, no changes to the six wheel robot.

Right?

And that's why I love it, because we spent so long making sure it was a really cool vehicle that we tested. Driving over the desk and playing on the floor and a lot of sort of, you know, action play that we did with it to make sure it stood up to the rigors of play. We never flew it.

Talking to Meehan is a bit like meeting the Tom Hanks character in the film Big, where a youngster inside an adult's body creates hit toy after hit toy because he can see things from a child's perspective.

We have a very structured way of being very innovative.

And here's dad. Nils Kristensen became the CEO of Lego in 2017. He knows the value of innovation. Half of the company's revenue each year comes from new products.

If you talk about having your head on the block as an innovative company, there's probably few companies that are as dependent on being creative and innovative all the time as we are.

Why is that? Is it the nature of kids as consumers? Yes.

Yeah. They really like something new. So we need to be there. We also need to be super relevant to what happens right now. If Wicked launches, then. And that becomes really important, then Liguist with Wicked. If Formula One is really creating excitement among kids, then there's a strategic partnership with Formula One and the LEGO Group, and we try to push into that.

But Christiansen also knows that unbridled creativity can lead to chaos. So a significant part of his job is channeling the enthusiasm of the designers. Big kids need boundaries, too.

It's not up to any designer just to design any new brick they would like to. We need to produce the bricks, and we want to produce the bricks as close to consumers as possible. I mean, we make billions and billions and billions of bricks every year, and they need to be produced just at the right time to be packed into just the right box just before the consumers need them. Because with 50% new every year, I don't have a lot of inventory sitting. So we produce it when it's needed, when it gets on the wish list. So how do you know? Now allow somebody to be super innovative and have all the wild ideas, while you restrict the complexity, on the other hand, so you can actually pull it off. Maybe it's the secret sauce of what we actually can do.

The secret sauce involves giving people like Dan Meehan the chance to let their imaginations occasionally run wild.

Once a month, we have this Fabulab Friday where all the designers, they can do whatever they want.

Fabulab.

Fabulab Friday.

Okay.

Basically means they can do whatever they want. I mean, whatever skunk project they have or whatever play thing they need to play out or do they just use that. So it's 5% of the working time where they can totally freely work outside any project they have because we need them to do that.

Meehan had not mentioned Fabulab Fridays to me, but he had talked about another period set aside for out of the box thinking. Come to think of it, at Lego, most things are out of the box.

We have what's called a Creative Boost week a couple of times a year where the whole design. Org will take a week to work on something. There's a free Creative Boost Week, which is early in the year. And that is, as the title says, where if you have an idea, you have a week make it, and then you present it to the whole design organizations. We have a conference room where we set up little posters and you build your model and you take it and everyone comes and everybody looks and there's been great ideas that have come through that. We then have a more focused Creative Boost Week where maybe we have an idea like LEGO Space, and we think, hey, we'd really like to make six to 10 products at a variety of price points and sizes and complexities, and we need some help, you know, so therefore, that's when you're saying, hey, space fans. Hey, anyone who's got a burning desire to make something, you know, come and join the team.

So far, so fabulabulous. But at Lego, Niels Kristiansen says every idea also needs to fit into a structured process.

So at the end of the day, it finds its way into exactly the portfolio representing all the different age groups, all the different themes and partners we have. We work together and just hit the portfolio we think is right for 2026.

Okay. All right. So that's your lead time.

That's our lead time in actually making it happen.

Yeah, because you mentioned a film like Wicked or, you know, so you have a sense of. Part of this planning is the kind of the.

We have more than a sense. We have total insight on what's going to happen.

Total insight. That's me told.

I think it's just interesting that despite being super creative innovative, we actually make decisions on what we think will be hot in 26. We do that by the. By the end of 24. So it takes a lot as an organization to really know our bets and make those decisions, be super clear on where we put our eggs.

Yeah. So the constraints at the front end on the designers are kind of that partnerships, the calendar that's coming at the back end, it's the modular materials that have to be made. And within that they can play as they wish.

Exactly. I think that mastering total creativity on one side and then really very, very structured supply chain and huge factories on the other side and making that gel in a way, I think that's quite special for.

As I left, Christiansen went to fetch what I assumed would be his card. But instead of an oblong bit of paper, he handed me a little Lego figure. We're looking at a little at a Lego representation of Niels. It says Niels B. Christiansen on the front. I mean, this is your business card, right? It literally is. The email address and phone glasses are there. Yeah. Pretty accurate. Yeah, it's good. I love it.

You could have argued they should have.

Longer legs, but you are quite tall.

But that's back to complexity.

Right.

Lego is very different to wave and monumental. The startups are trying to make a brand new technology work. At the toymaker, innovation has been industrialized into a potent potpourri of playfulness and planning, creativity and calendars. But there are echoes between them. A clear sense of purpose and whether building walls or driving cars or making bricks, a focus on feedback and a mixture of iteration and new ideas. If these strands connect firms at the start of their journey and titans of their industry, what about a company which has to innovate in response to the challenge of a new technology?

Stand clear of the closing doors, please.

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The breadth of what people come to for search like continues to leave me in a sense of awe.

Liz Reid knows what you Google, though not in a creepy way. She's the tech giant's head of search.

If you go back to our mission of, like, organizing the world's information, people often, like, stop right there, organize the world's information. But there's like, the second part about making it universally accessible and useful. And useful is a very different bar. Right.

Reid's task is to get the world's most popular search engine prepped for the era of generative AI.

You can have a medical book in a library that you can check out and it can be available to you, but you may not understand any of it. So great, it's accessible, but who cares, right? And so we constantly have this question about how can you make information more useful to people? And so what we thought about a lot with AI overviews is what are the types of questions that are either not really possible or they're really hard for people to get a good understanding after, and how do we change them?

AI overviews are the most visible manifestation of Reid's work on AI in 100 countries. And for many search queries, Google's large language model now generates a summary answer at the top of the page. The feature is changing the sorts of questions that people ask.

What you see with generative AI is that we'll often talk about people asking longer queries. And the question is, why do they ask longer queries? Well, they ask longer queries because they often put more of the pieces of their task in the query. Right, okay, so instead of it just being, okay, I'm going to search for dog friendly campsites, and I'm going to search for kid friendly campsites, and I'm going to search for affordable campsites. I'm like, I need an affordable campsite that is available in this time and allows dogs and will be okay with my kid in a stroll. That's actually the question they had.

Why not just search? How can I have the worst possible holiday? The obvious problem for Google is that a good AI summary risks fewer people clicking on the links that power its revenues.

This question of the ecosystem sometimes has this myth that users want either an AI overview or a link, and that's actually not what we see. What we see that actually a lot of people like about AI overviews is the fact that they can get both.

AI overviews first went live in America in May 2024. But the story began long before that with Google's early work on large language models.

Different teams sort of had demos or little projects together. And then at some point we sort of started to pull them together. And we had more of a sense of what the product is. And I think that's often the case with innovation, where, like, people will be like, aha. There was this, like, one aha moment.

Some of us haven't had an aha moment since 1985.

It wasn't like there was nothing and then there was an immediate aha moment.

Right?

You're scratching the surface, you're finding out things.

There were multiple versions of the overviews.

In the months that we were building it. It went through many iterations. You would figure out this felt great, this felt too hard for people, this was confusing, this was too long, this was too short. Search is really interesting because people have an expectation that they can get information very quickly. And so one of the things you're trying to do with AI overviews is figure out that right blend of ensuring that if you came in with a question, you can get to that answer as quickly as possible. And also, people like learning a little bit more than they necessarily asked.

Right.

And so what's that? Right balance, even within the response of the clarity. When should you elaborate? How do you help people have ideas of what to do next?

As ever at Google, user behavior told the team what was working and what was not.

If you put something bad at the top of the page, this will not cause people to use search more often. This will cause people to come to search less often. Okay, so that can be a signal. How do they engage if they're not happy with results, they often try again. They issue another query. Okay, so if you're shrinking the number of times that a user has to, like, issue a query and then try again and try again, that's usually we've seen years and years over. That's a sign that people are happier.

The questions people asked also guided the team's efforts. And some of those early questions caused the AI to give answers so weird that they were newsworthy.

Some of those questionable, bizarre results centered around the AI overview. Telling users that you can eat a certain amount of rocks a day for your health. It's healthy to do so. To be honest, if you asked me before, are people going to ask us, how many rocks should I eat a day? I don't go like, yeah, that seems like a question on top of people's minds. But then they ask that question. You're like, oh, I need to go do something about that. And this is something that's been true well before. Generative AI and search. Right. You have a known sort of area where you think you can improve. You open something up and then people start asking new questions in that space and then they surprise you with additional questions you never thought of. Nobody searched for opening hours 20 years ago on Google because their belief that they would get opening hours was basically nil. Right. So they just didn't ask that question. And then you started to do it, and then people asked more and then they were like, yeah, but what's the opening hours for the breakfast, you know, menu? And you're like, oh, my gosh. Okay, this is a new question.

Reid is trying to anticipate a fast moving future, but she's keen to emphasize that answers may be found in the past.

I try and be careful, especially because Google's had a long tenure about, like when somebody says, we've tried that before, most of the things that I think of as being really successful at Google, or at least in the search and map space where I've worked in, they were tried two or three times before and failed. Right, okay. Because the instinct was right, but the tech wasn't ready or the data wasn't ready or something wasn't ready.

Like what, what do you have in mind there?

Right now on Google Maps, we have quite a lot of reviews from users and photos from users. We tried that for years before we could get any traction. And then two things changed. The combination of notifications and understanding where a user was. So if I go to you, Andrew, I'm like, hey, Andrew, would you like to review all the restaurants that you've been to in the last two months? You're like, oh, now I have to think of the restaurants and I have to search for the restaurant one by one. Oh my gosh, this is a lot of work.

Not really. I had about 800 pret a manger sandwiches, but imagine I had a more varied diet. And Reid's point is that an old idea can suddenly have its moment.

And we know that you went to the restaurant yesterday. And we can alert you and say, hey, would you like to write a review for that restaurant? Ooh, that's a lot less work. Right. You just click the notification. You click five stars. Whoa, you've given a rating. And so the bar of friction was really high for us before, and we couldn't do that. And then suddenly the bar of friction for the user got way lower and we were able to interact with users.

Data obviously matters to the iterative process Reid describes. This is Google, after all. But she says there's still room for intuition.

I do think you have to mix it with a Set of instinct because you're charting new territories, right? So you can't just look at data before. I think one thing that stuck with me that one of my UX designer peers years ago told me is like, make sure you're falling in love with the problem, not the solution.

Falling in love with the problem, not the solution is a nice aphorism. I can imagine repeating it to people as though I came up with it myself.

So if you're really looking at what is the thing you're trying to unlock, then you want to be obsessed with that. And so you don't want to just focus on, here's your first idea to do it. You want to get focused on testing does the solution actually solve the problem for that? I think a lot of the instinct, the question I sometimes push people on with that is like, what is the hypothesis behind the instinct? You can't always measure everything, so you can't always have data. But the instinct is like, this is hard for people. Or watch somebody do this and see why it's difficult. Or if I could unlock this, this thing would happen.

Liz Reid's advice reinforces many of the messages from the other people I've spoken to. Try things out, get as close to the customer as you can and be prepared to get things wrong.

I'm just trying to think of our very, very first pilot we did. We didn't even know what to look for. So we focused a lot on wall quality, obviously, because that was important.

Back in the Netherlands, Salaar Al Faji is an evangelist for this approach. Being out on the building site, seeing what his robots do in the wild is the way Monumental learns and improves.

And then we realized we actually missed two or three key things, including the staining of the bricks. Just like visually their color, it sounds so stupid as I say it right, but like, you're just like focused on like, where do the bricks go? What do they look like? And then we realized like the way we built the wall, the way we laid the mortar, even if you cleaned it up afterwards, like half the bricks were like stained and had mortar on them which you could not remove.

There's no one way to innovate. Lego is churning out new products with a two year lead time wave. And Monumental are moving towards a founder's vision. Google is overhauling the world's most popular webpage in midair. But there are common themes. One, forget the 1980s earworms. Breakthroughs very rarely come from aha moments. You often have to work towards something for years and you don't know when the market will be ready for it.

2.

Iterate and innovate. A portfolio approach pays dividends whether you do it formally or not. Give people space to work on big new ideas as well as improvements to existing ones. 3. Test and learn. Whether it's a building site or London streets or playing children, get feedback and be ready to change course. There's a phrase that I found useful for at least the past 10 minutes of my career. Fall in love with the problem, not the solution. Next week I'll look at the aspect of office life that causes people the most stress. Public speaking.

Beautiful. Give me one more I'm going to not look at you this time.

In this season of Boss Class, we're releasing an extended interview to accompany each episode. On this week's Meet the Boss interview, I talk with Liz Reed of Google and find out more about how people search, why dog food matters to the firm, and what three year olds can teach all of us.

They ask whatever question comes to mind because they don't worry about how hard it is to answer. They assume you know everything.

And coming up on the rest of this season of Boss Class, I've seen.

Many leaders and managers over my career that get so stuck in the here and now as opposed to let's look around a corner.

And I think what the jazz band does is have an alternative for dealing with complexity. Things are going to go wrong.

The question is, what happens next?

What's the next note? So what's the secret of a good negotiator? You just have to be able to shut your mouth and listen to. Well, to listen well, you'll need to be a subscriber right now. You can sign up for half price, just a couple of dollars a month. Search Economist Podcasts plus for our best offer. We'd also love to get your thoughts about what you've heard and what you'd like to hear on the show. Email us@podcastseconomist.com with the subject line Boss Class. Boss Class Season 2 is produced by Alicia Burrell, Sam Colbert and Pete Norton. Our sound designer is Weidong Lin. Original music by Darren Ng. The series editor is Claire Reid. Our executive producer is John Shields. I'm Andrew Palmer. This is the Economist.

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