We all know about the nimble startup that outflanks the big guys. But how can larger institutions take advantage of the same cultural phenomena as their smaller, newer counterparts? In this presentation, Undercurrent founding partner Aaron Dignan juxtaposes the characteristics that make businesses (and some aspects of nature) last for the long haul.
The most successful companies of the digital age display characteristics of what Dignan calls a “complex adaptive system.” They are networks instead of hierarchies, they process information rather than manage it, and they adapt rather than sustain. By thinking like a complex adaptive system, Dignan says, “you will be able to handle complexity and scale and you’ll be able to adapt in a way that your competition won’t.”
Aaron Dignan dressed up like a super hero for 180 straight days of the first grade, which marked the beginning of his life as an iconoclast, observer, theorist, and performer. Now, as a founding partner of the digital strategy firm Undercurrent and based in New York, he advises global brands and complex organizations like GE, American Express, Hyatt, and Ford on their future in an increasingly technophilic world. Aaron’s first book, Game Frame: Using Games as a Strategy for Success, was released in 2011.
That typical and appropriate title– be responsive. 10:30 this morning I found out that we got a chance to exchange some information.
So I work day to day with some really large institutions, organizations that we think of as important and the bedrock, the foundation of our culture. Big employers. Big nonprofits. And that means that we’re forced to deal with a lot of issues of scale. And one of the things that these organizations have been paying attention to, and all of us have been participating in, is this movement around startups and new technologies and emerging ideas that have been shaping culture, I think in an accelerating, and an even more meaningful and impactful way over the last few decades. And so I wanted to start with just an observation that’s obvious to everyone in the room. And that’s the idea that, at this moment in time, it is easier than ever to bring an idea to life. And it’s easier than ever to bring a product to scale. So you can take something that’s in your mind’s eye. Bring it into existence. And then you can get that a billion users in a very, very rapid time frame. And that’s incredibly exciting and powerful, especially if you have a great idea and you have a great team to execute it. But we started to pull at that and figure out why is it that that’s possible now and it wasn’t possible before. And there’s basically two really good answers that all boil down to basic trends in technology. So one of those is this idea of platforms. Right? So people now can leverage other people’s ideas and other people’s technology in a way that was more difficult in the past. It happened. Somebody invented the wheel and the rest of us were like, wow that’s really good. I’m going to use that on my tractor. I’m going to use that on my plow. I’m going to use that on my carriage. But it didn’t necessarily have the exponential impact that something like Amazon Web Services does today, where I can actually build in someone’s back end. It’s completely robust. It can handle the traffic. And I can go from nobody to somebody very quickly without having to invent the entire business. So a lot of people in this room have started to build skills that help them work on just the front part of the spear. So that when you lean on these other networks, when you go to fundraise on Kickstarter, when you put your back end on AWS, when you sell through e-bay or Etsy, or what have you, you’re leveraging that platform. And the other idea that’s highly related is this idea of networks. So for the first time in culture, not only are we all connected as individuals, but our technology and our products and our things are starting to talk to each other, as well. So you have this whole movement of the internet of things. You have this whole movement of social media and social connection. And now suddenly these networks are just there, waiting for us to take advantage of them. So if you have a really good idea about how someone should do something, like, let’s say, take pictures on the phone and share them with friends, you can leverage the network of people, and mobile phones, and GPS data, and photography technology, and the platform that exists, and build a billion dollar business with a dozen people in a couple years time. So that’s really incredible. And it means a lot to people that are coming up, that are starting, that are new. An example of this that I love is Bre’s story. So MakerBot, the company most people in the room probably already know. How many people have seen or touched or played with a MakerBot? All right. It’s a good crowd for that. Don’t always get the same answer depending on who you’re talking to. But, essentially, this story is a story of these ideas. So Bre is participating in what is essentially a Brooklyn technology meet up. They start talking about 3D printing. They start talking about the fact that 3D printers as they existed, even a few years ago, were by and large out of the reach of the everyday person. And so they go about the work of making one for themselves. They can’t afford to And, what’s more, they can’t share that with everyone. So they build what was ultimately the Cupcake one. That’s the one in the middle there. And it works. And they’re able to leverage existing tools, and cutting technology, and chips, and arduino, and all that stuff to assemble something based on other people’s platforms. And then when it’s ready, it turns out there’s a market for it. There’s actually a network of people out there that are identifying themselves as makers, as innovators, and they want to participate, as well. And so it becomes a product business. And it starts to scale. And it gets funding. And the printer gets better and better micron resolution. And yet the price point is still staggeringly low– it’s a couple thousand bucks– by comparison. And so the business grows. And then they realize that as people use the printer, they’re creating files that allow them to share products, and the designs for physical things. So they create Thingiverse, the website that lets each of these people in this network share the IP. So if I create the world’s best toothbrush, which is a thing, then I could share the world’s best toothbrush with you. And you could print it at your house and refine it. Maybe change the handle if you have small or large hands, depending on who you are. And so that idea of this network of the exchange of ideas of IP creates a really valuable entity. A really valuable movement around making in this new decentralized and open and accessible way. And then what happens at the end is that the big 3D printing company comes in and buys it because it’s so interesting. It’s such a beneficial synergy. And it’s so competitively threatening that they come in. And essentially, what they could have done is the big printer company, Stratasys, could have gone back in time, given these guys a free printer, given them all a job, and saved themselves So this is happening. And it’s happening all the time. And I don’t want to beat a dead horse because you guys see it. You’re participating in it. You’re touching it. You’re using it. You’re driving this forward. But what I do want to share is the impact that this kind of thing has been having on the larger institutions. The ones that most of our friends and family and parents work for. The ones that really keep the trains running. And so take a look at this. This is the S&P of time for a company’s So if you go back in time, pre-1960– so that’s 60 years. So if you get to the top of the heap, good place to be. Because you can spend one, two, three different careers, essentially hovering at the top of the heap. Being big. Being important. But it starts to erode over time. And you see with each successive wave, it gets shorter and shorter. And now we’re basically talking about somewhere between 12 and 15 years of tenure is all that you’re likely to get if you get to the top of the pack. Right? And this is research that was done by Richard Foster. And it’s backed up by the entire history of the S&P. So we see that clearly there’s an issue around adaptivity with big organizations, with large networks. And so they have to change faster. And that’s usually where we start our conversations– is we come in and we say, hey, there’s all sorts of amazing things going on here, but you’re too slow. You have to change faster. You’re not able to adapt and be responsive enough to the market if the outside world is moving faster than you are. Then you’re in trouble. Right? So we say that. But the look that we get back is– that’s impossible. It’s so difficult to move quickly because we’re so big. And so the second challenge that earlier stuff creates and presents is this– so ladies and gentlemen, I present to you, your health care system. Why was healthcare.gov a disaster? Well this is why. This isn’t even the people that are involved. This is just the departments and the stuff that’s involved. The systems. Right? So an incredible amount of complexity here. And if you’re trying to make this system go faster, or be more fit, or adapt to an environment, then you have to understand that whole system. And what’s really difficult about that is when you get with some data jockeys, and you start going into the math of what kind of complexity you have to hold in your person company, or a company that operates in 48 countries or 50 states with different rules, what kind of complexity does that create? The answer you get back is– too much for any set of human minds to hold. So unless you’re going to give this over to a really, really powerful Watson-like Jeopardy playing computer, the design of this organization is largely out of your reach. And yet that’s exactly what we do. We give the puzzle of healthcare.gov to a group of people. And we say, figure it out, and when you have the answer, tell us what it is and we’ll build a hierarchy of people to go execute that mission. And in three years when it isn’t working, we’ll all come back together again, write a big check and reorganize the business. And start over because we’ll have to rethink what’s happened. And so that is essentially what we’re up against– is this back and forth between the need to go faster at scale, and the challenge of complexity. How do you make sense of all this data and information? And so you have to figure out a way to hold in your mind all the things that are going on at the edge with every user, with every situation, with every problem. And if you do that, you end up exhausted and you’ve essentially failed. So once we recognized that and we realized, OK, all of our clients, all of our partners, all of our investments, as they scale– even the startups get big really fast these days– they run into this problem. And it starts to really stress them out. And then they essentially fail at being adaptive at the edge. And what it means is that employee engagement, and the way we all feel, and the movement around that thing gets bad. And when it gets bad we all get demotivated. And it feels like shit. And we go start another startup and start over. So the idea here is, how do we fix that? So we started looking. And the more we read, and the more we talked to people, we started to realize that there is a set of systems out there that are not always related, that are not always clearly the same, but that scientists and physicists would essentially call complex adaptive systems. And complex adaptive systems mostly exist in biology and in the larger world. They exist in places that you’d sort of expect them to be. But they work in a fundamentally different way than our organizations do. And so I just want to show you three complex adaptive systems that you already know and love. The first is ants. OK, maybe not love. But you love the other two, I promise. So the complex adaptive systems– the ants, right? The ants have a queen in the middle of a colony. But the queen issues no orders. Right? The queen does not have command over the colony. She simply gives birth to the colony. She is the genetic record keeper for the colony. But after the ants are born, they have to figure out what to do on their own. And what’s really challenging about that if you’re an ant is that they have a very limited language. So it’s not as if they can write a business plan for the colony. They can’t write an architect blueprint for the colony and say, all right this is what’s going to happen here, and this is what’s going to happen there. And are you on the same page, Bob? Yes, I’m on the same page, Bill. It can’t happen. Because essentially all they can do, is they can leave a trail of pheromones behind them when they walk. This is where I’ve been and where I’m going. And they can use their antennae to say what work they’re doing. I’m picking up trash. I’m collecting food. I’m dicking around. And so– there’s very little dicking around in the ant kingdom. So, essentially, that’s what they can do. And yet there is astounding complexity in the ant world. There is a woman by the name of Deb Gordon who’s been studying ants for 29 years, which is focus, by the way. And what she’s found is that they’re able to do things like make sure that the garbage pile is equidistant from the colony’s entrance, from where they put their food source. And that their eggs and their babies are always in a very protected and precise place in the ground. And that the nature of the way the movement of the ants occurs is essentially scientifically perfect for foraging across a large range of land with the optimal amount of time and energy. So there’s a lot of really smart stuff going on. And yet there’s no smart people making those decisions. It’s just emergent from the rules that define how they interact with each other. And the way it works is that, essentially, if I’m an ant and I cross a certain number of other ants who are all going in the same direction, then I probably go in that direction, too. And if I see a bunch of ants coming back from there but they don’t have any food in their mouth, and they say, I’ve been looking for food but I didn’t find any, then I avoid that area. So there’s very simple rules that define their behavior. But from those simple rules emerges this really complex society. And it’s so complex and so intelligent that a colony that’s five years old is different than a colony that’s 15 years old in the way it behaves and the way it works in its environment. The 15-year-old colony might be a little bit calmer, a little bit less aggressive, a little bit more localized, or might go out less when it’s cold. And what’s amazing about that is they don’t have any written record of how they got that way. All the ants then were not alive when the other ants were running the show. They have no history of how to do it. They have no business plan. So they’ve essentially just magically changed their behavior with the exact same DNA and the exact same instructions. So it’s incredible what they’re able to do. And they’re very, very adaptive. And right now, when we see things like honeybees really struggling in the environment, ants are doing pretty well. Even in intense drought they figure things out and make it work. So they’re incredibly resilient. The next one is the immune system. So the immune system is incredibly similar to the ants in the sense that it has to go out and protect you from all these different pathogens. And it doesn’t know which pathogens are going to be bad or good. And so it has to figure out a way to do that. So the way it does it is they create millions and millions of different lymphocytes that are out there with different receptors just looking for a friend. And when they find a friend, which is a pathogen, they hold it. And they release a little signal. And if enough of them catch that pathogen and release that signal, they’re activated. And they creep back up to the lymph nodes– you know how you get swollen lymph nodes. And they reproduce really quickly because they are the equivalent of the Uber of the startup world. They have found a pathogen. It’s a product market fit. And, holy shit, they have to replicate and scale. And so that’s exactly what they do. And all the other lymphocytes keep doing what they’re doing. They keep sending the variety up because we want to keep searching for the next big thing, which in their case is a pathogen, which is bad. And in our case is a great idea for society, which is, ideally, good. And the last example I won’t spend any time on because I know who is in the room is the internet. The internet is also a complex adaptive system. And it has all these similar traits about the way it’s resilient, the way it communicates, the way it works without a central authority– although god knows people are trying to be a central authority for it right now. But that won’t work because that’s not the kind of system it is. So what I wanted to do is share with you guys very quickly the few traits that define a complex adaptive system, held in contrast against the way we think about most of our modern businesses. So that you can see the difference. And start to think about, in your own role, in your own team, in your own department, in your own business– how can you start to migrate in the direction of these systems that are so resilient, so intelligent. And able to do things at scale that no hierarchical system can do. So that you, as the leader, can let go a little bit. And let self-organization and emergence take hold. And watch some of the remarkable stuff that happens. So the first is hierarchies to networks. People talk about this a lot. And I think even earlier today there was some talk about networks. Networks are the fundamental building block of a complex adaptive system. You can’t have one if you don’t have a lot of constituents running around. So we have to build networks. And as businesses, we have to think differently about the barrier between us and them. If I’m Airbnb, as a host part of Airbnb, or a customer, or a user, or I don’t know, it doesn’t matter. Because they’re part of the network that makes this thing work. And the same thing is true of a user, and a customer, and a partner in a restaurant down the street. We’re all part of the same system in that context. And we’re either trying to make that work, or we’re not trying to make that work. And so a network is a really important idea. The next one is this idea of moving from central control to self organizing– to decentralizing, to giving authority away. And all these ideas that you hear about– about distributed authority, and autonomy, and giving teams what they need to do their work at the edge. That’s what this is all about. It’s about letting the ants decide based on what they see at the edge, what they detect, what to do. And if we can’t trust and empower people in this way, we’re always going to be too slow. And in this day and age, being too slow is essentially a death sentence. The third is this move from complicated rules to simple rules. And my favorite analogy for this bucket is just culture. Companies that have great culture have created simple values, and purpose, and a sense of alignment that is enough that they don’t have to micromanage all the little decisions. So they can be de-centralized. My favorite example of this is Zappos. So Zappos has a really long and well thought out culture book. And it seems overwrought to some people. But to me, I think if I have to give people a book of ways of thinking and how we are as a culture, and then trust them to do their job without any interference, I want it to be well thought through. I want it to be robust. I want it to be deep and rich with meaning. And so ideas like strong values and strong culture really drive this transition. And allow you to be simple. The fourth is moving from managing information to processing information. Most of the big companies that we interact with on a day to day basis have a lot of data about us. But they don’t do anything with it to make our lives more enjoyable. In fact, they do things like, you call, can I have your number please? Your account number? Yes, it’s 8864532. Thank you, transferring you. Hello. Hello. Could I get your account number, please? It’s like, not only did you already have it, but I gave it to you again. And now you still don’t– there’s just no use of information. And yet on the right, something like a Google self-driving car, is an information eating machine. It basically takes every piece of data that it can sense, and uses them all to make sure that you don’t die. And so it’s incredibly powerful in the sense that it wastes nothing it. It processes everything. And we want our systems to do that. We don’t want to sit on data about what our users need, what our people want, what inspires our employees, and not use that data to make the experience better. And if we have systems where individuals are allowed to do that, then the net net is that we’re going to get improvement over time. We’re going to get faster adaptivity, which is the final transition from sustaining– which legacy businesses want to do. They want to protect and try to keep their pie the same size. And make sure they hit quarterly earnings. And do lots of things that are protective and risk avoidant. And yet in the midst of a world that’s changing this fast, if you’re not constantly learning, if you’re not constantly getting better, if your business isn’t the equivalent of a really well-tuned Nest thermostat that is always looking for chances to tweak– and giving people the power to do that, to change their rules and change their rules at the edge where they have information, then you can’t move forward. And so one final example that brings all this together that I really, really like is Waze. So how Waze users in the house? OK. My gift to the rest of you is if you’re not using Waze and you ever drive, use Waze. In fact, on the way here, I was using Waze while my driver was trying to figure out which way to go. And I was like, no, no, no. Go left on 29th. There’s no traffic there. We’ve got to get there. So it’s amazing. And here’s what it does. It takes a network of phones with GPS data, which is a platform that someone else built that they leverage and get on top of. And it creates a system where that network optimizes and is self-organized– people decide where to drive, they drive in clusters, the information is shared. They use simple rules. You can only tell it a few things. There’s traffic. There’s no traffic. There’s a cop. There’s no cop. And they actually build a model where now they’re going to use that data, process the information, to give me the best possible route. So instead of going this way, where the traffic is slow, it’s going to send me this way, where the traffic is good. And finally, as a system overall, it’s adaptive. So that it’s moving all the cars around in the best way possible. So everyone gets where they need to go. And if everyone in the city was using this at the same time, the amount of time we’d waste sitting in traffic would go down precipitously. And that makes a lot of sense. And on top of that, if we had self-driving cars that thought carefully about when to accelerate, and when to decelerate, then that would save even more time. And so it makes a hell of a lot of sense that a network of self-driving cars– Google– bought a network of this information. And starts to bring the two together. And you can see that a company like Google and a company like Waze are thinking like a complex adaptive system. And that’s why they’re experiencing such success in the marketplace– is that they’re moving in the direction that the world wants to go. And so it turns out that when you do the research, there’s a lot of companies we’ve found so far, that in some way, shape, or form reflect the ideas of a complex adaptive system. They are responsive companies. They have a responsive operating system at their core. They are not perfect. They’re not nearly as perfect as a hill of ants. But they are starting to bet on these ideas in some departments, in some ways. And the expression of that bet is that they’re doing incredibly well in the market. So well, in fact, that we’re talking about a half a trillion dollars on the slide alone. They’re really doing meaningful work. And the fact that it’s starting in software, the fact that it’s starting in the startup space, means that some people poo-poo it. Some people look down their nose at it, or say that it doesn’t matter. It’s just photo sharing. It doesn’t matter. But to me, what I see here is not the work that’s being done, but the way it’s being done. And how impactful and how meaningful that could be if it was applied to health care. If it was applied to education. If it was applied to transportation. If it was applied to food.
So there’s actually something going on here that we can learn from, even if we don’t like what these guys represent. And that’s really what I want to leave you guys with. These are the five ideas that any of you can take to any of your businesses, to any of your contexts, and actually start to drive some of this stuff home. And if you just do these things, even a little bit better than you did yesterday, you’ll be more responsive. You’ll be able to handle complexity at scale. You’ll be able to adapt in a way that most of your competition won’t. And you’ll achieve what you intend to achieve. And while you’re doing it, the entire network of people that are involved will be excited and engaged and right there along with you. So thank you. [APPLAUSE]