Dan Ward, co-founder of SparkLit Group and author of Lift, opens Day 2 with a three-part toolkit for navigating territory nobody has mapped. From the past, he takes Octave Chanute's 1894 book Progress in Flying Machines, a record of 400 years of failed flight experiments that the Wright siblings used as their starting point, and draws a two-word lesson: study failure. From the future, he introduces strategic foresight and walks through four archetypal scenarios for AI in GovTech, covering growth, constraint, collapse, and transformation. From the present, he shows how small, observable signals become the raw material for those scenarios, so you can build toward the futures you want and away from the ones you don't. He closes with a juggling demonstration and a short reading list.

All right, so let's get started here. And we're going to begin. We're going to talk about the past. We're going to begin by talking about the past. In 1894, an American civil engineer named Octave Chanute wrote a remarkable, remarkable book that changed the course of human history. This is my copy entitled Progress in Flying Machines. And in this book, he documented 400 years worth of failed aviation experiments. Everything anybody had ever tried in the history of mankind's longstanding dream of flight, he wrote about it, he laid it out side by side, and he compared this did not work. This too did not work. This the guy also did not work. And he optimistically titled his book Progress in Flying Machines. Now, at the beginning of the book, he gave the three reasons why he wanted to write this book. The first reason, and parenthetically, this is the best reason to write a book. Where's Bryon Kroger? Great reason to write a book here. The first reason he gave was to satisfy his own curiosity, to satisfy his own curiosity as to whether it is even theoretically possible to build a machine that could take a human could climb on board, take off, climb into the sky, navigate through the wild blue yonder, land safely, do that repeatedly. Was that even theoretically possible? Or did flying machines belong in that same category as perpetual motion machines of machines that violate a law of physics? You can't actually make one of those. Chanute did observe that no bird weighs more than 50 pounds. I don't know if that's strictly true, but he says no bird weighs more than 50 pounds. Try not to bump into my mic here. And so maybe there is a weight limit to flight, but he correctly concluded.

I think flight is possible. He correctly concluded based on the available evidence, based on 400 years of failure, yeah, I think we can solve this problem. And in fact, I think we can do this soon. I think we're getting close. I think we can do this in my lifetime. And in 1899 when Wilbur went to the Smithsonian and said, "Hey, we want to build a flying machine. What should we read?" This is the book. This is the book that they gave Wilbur and Orville and Catherine. All right time out, time out. Meet me over here for a second. Orville and Wilbur had a sister. Her name was Catherine. Wilbur said, "If humanity remembers us in the field of aviation, it must remember our sister Catherine." So we promptly call them the Wright brothers. And so I try to refer to them as the Wrights or the Wright siblings if I'm feeling sassy. A French newspaper at the time actually referred to Catherine, they were so close, referred to Catherine as the third Wright brother. Okay, so we admit she existed, but they didn't quite get it right. So today I try to refer to them as the Wrights or the Wright siblings. All right, time out. We're back to the message. Okay. So the second reason Chanute gave for writing his book was to identify the dead end paths, was to figure out what are the things that we are doing that will never work and we should stop doing them. So for example, he correctly determined that we should stop gluing feathers onto our wings. Gluing feathers onto wings, ladies and gentlemen, is not a thing that we need to do. We can stop spending time, money, resources, and attention collecting a bunch of feathers and gluing them onto our aircraft. It seems obvious, but people were doing that in the late 1800s. Second thing he figured out was that flapping wings were a bad idea. This was a more radical conclusion. Flapping wings were a bad idea. See, because birds, bats, and bugs all flap their wings to fly. This is the only successful model of flight that we've ever seen is flapping wings. But Chanute sort of laid out all those stories and he mathematically, rigorously, comprehensively compared them all. He correctly came to the conclusion flapping wings are a bad idea. Static wings are the way to go. Okay, so the first reason it was to satisfy his own curiosity. The second reason he wrote the book was to identify the dead ends. You can probably guess what the third reason for writing his book was. The third reason was to identify the most promising paths, to figure out who is heading in the right direction.

Look, nothing has worked so far, but are some people making progress, thus the title of the book, that maybe we can build on and learn from. For example, he correctly determined that curved wings produce more lift than flat wings. Curved wings produce more lift than flat wings. That should just be math. You'd think that would just be.

Rachel Del Vecchio:

Hey Dan, I'm going to interrupt you really quick so we can fix your mic because we know we need you to use your hands in two minutes. So I'm going to hold this and they're going to go back there and fix with you. Okay. And I'm a dancing monkey on the stage. Oh, do you guys think I could do this? No, no, no, no, no, no, no. You guys, do not encourage me. I can't even balance it. There we go. Okay, awesome. I wish I had a few jokes queued up here. Let me think. I know I heard one from my daughter the other day.

Tell us about the jacket... That was quite the statement piece. I've been making it for two weeks. I have a three year old who helped in some ways, made it a lot harder in other ways. But yeah, if you guys remember, I said in the back in prod, we trust, which is one of our fun little slogans we say, obviously related to production.

Are we good?

Dan Ward:

Ear any good jokes lately?

Rachel Del Vecchio:

Yay. Okay. I will get out of your way.

Dan Ward:

All right. Sorry about the mic. This should be better. All right. I think it's not going to click like that before. Okay. Where was I? The third reason he was going to give for writing this book was to identify the promising paths. So curved wings produce more lift than flat wings. That should just be a matter of math. That's just physics. It should have been an easy question to answer, but in 1894, nine years before the Wrights had their first flight, this was still an unanswered question for two big reasons. The first reason does come down to physics, and that is there's more than one way to curve a wing. And some curved wing shapes produce more lifts, some curved wings produce less lift. And so if you compare a badly curved wing to a flat wing, the flat wing will produce more lift.

The other physical challenge here is that a perfectly flat wing, once you get some air moving over it, will bend, it will buckle, it will change its shape a little bit. And so there were a group of people trying to say, "How can we make it perfectly flat and keep it perfectly flat to maximize our lift?" But the second thing that made this hard was geopolitics. See, there was this group of German engineers who were convinced that what we now call the air foil shape, the kind of the classic curved airplane wing shape, that produces more lift than a flat wing. And there was a group of French aviation enthusiasts who said, "No, no, no. The more perfectly flat we make it, the better it flies and the more lift we produce." The Franco-Prussian war had just ended. And so the Germans were like, "We don't want to share our data, share our designs with the French." And the French are like, "What do Germans know about engineering?

Why would we listen to the Germans? They're not going to share their information with us anyway." So geopolitics got in the way of making a decision that should have been a science, physics, mathematics decision. Thankfully, we are more evolved than that today and politics never gets in the way of science and physics. Okay. The reason I told you all of these stories is that in 1894, the people who were designing, building, launching and crashing flying machines in these unsuccessful attempts to fly, they were trying to solve a problem that no one had ever solved before. It's a remarkable thing to decide to do. For 400 years, people have been trying to solve this. None of them did it. Sign me up. I feel like the people in this room are of that type of mindset. When we are trying to solve a hard problem or trying to solve a problem that's never been solved before, we are facing unchartered territory.

There is no playbook. There is no easy solution. There is no set of best practices. Just go do it this way because nobody's done it yet. And so in his book, Chanute does point out that failures are more instructive than successes. And good news everybody, there's been more failures than successes, and in fact there's been nothing but failures. All we have available to us is the most instructive resource, which is a bunch of failures. So here's a picture of Chanute and one of his gliders flying over the hills near Lake Michigan. He was rather proud of his safety report. The only reported injury was a rip in his son's trousers. I want one of those gliders so badly I can taste it. All right. So again, the reason I'm telling you all of this is we want to provide some practical, usable, domain agnostic methods and things that you all can do when you are facing hard problems and facing challenging situations, maybe even unsolved problems.

And in two words, the advice here is study failure. Study failure. And Chanute's example in his book, which is you can find the PDF online for free and download it. It's a great book. This gives us a model of what it looks like to study failure, to lay it out side by side, mathematically evaluate them, look at the whole spectrum, do the math and say, is it theoretically possible? What are the dead end paths we can stop following? And what are the successful, wrong word, what are the promising paths that we should pursue? But what I really like about study failure as advice, and my general rule of thumb is that advice should be counterintuitive, advice should be telling people to do stuff that they wouldn't naturally do otherwise, is that study failure, that's not what we usually do with failure, is it? Maybe you all do, but other people who weren't smart enough to come to Prodacity, other people might say failure next. Oh, that failed. We will not speak of this. Oh, that failed. I'm going to move on. Oh, that failed or no, it didn't really fail. It was kind of a partial success and we get mely mouthed and we get dishonest with ourselves and others and we don't hold our failures up to the light and say, hey, this failed. Let's take a closer look at that and let's look at all of them and figure out the answers to these three big questions. Part two, let's talk about the future. When we think about unchartered territory, the future is the ultimate unchartered territory. I met a guy whose job title was a futurist. The first time I met somebody whose job title was futurist, he told me there are no facts about the future. And my immediate reaction was, wow, being a futurist must be either the easiest job in the world or the hardest. There are no facts about the future. The future is surprising. The future is unpredictable, but although the future is surprising, the fact that the future is surprising should not be surprising. We should expect to be surprised. I don't know the details of the surprise. I just know the fact of that will happen, but there's one thing that we often get wrong about the future, and I've done it just here in front of you all on purpose so I can make this point, and that is that the future is plural.

There is no the future. There is only the futures. The future is the realm of possibilities and things that unfold in front of us. There aren't any facts about them, so we can't talk about the future. We can only talk about the futures, plural.

So there's a field of study. There's a field of practice called strategic foresight or futures thinking, and there's a number of futures thinking tools and techniques. And I'm not going to make you an expert on futures thinking methodologies in the next five minutes, but I will introduce to you to one kind of core essential practice of futures thinking, and that is building scenarios. So a scenario is kind of a term of art in the field of strategic foresight and futures thinking. A scenario is an imagined story about a future, an imagined story about a future. A scenario is not a prediction. We're not saying the future will unfold this way. A scenario is not an expression of preference. I hope the future unfolds this way. A scenario is just a description of how the future might go. Now, there are four archetypical scenarios that people often build. I mean, there's a million other scenarios you could build, but here's four archetypical future scenarios that you could consider putting together as you think about the futures that are unfolding in front of you. They are growth, constraint, collapse, and transformation. So let me walk you through a quick example. The growth scenario, and let's imagine we're going to talk about some futures around, let's say, picking a random topic, AI and GovTech. A growth scenario for AI and GovTech is we look around and we observe a lot of people are using a lot of AI tools and a lot of domains and a lot of different applications to do a lot of things. The growth scenario is that trend continues. More and more people using more and more AI tools to do more and more things and more and more applications and more and more, and just the more continues.

So we might then ask ourselves some questions, why does that growth continue? What enables that growth to continue? What are the things that would be necessary to make that sort of trend, that growth possible? Then we might ask, what do I like about this scenario? What are the desirable attributes about this growth scenario where more and more people are using more and more AI to do more and more things in this GovTech space? Then what are the undesirable elements of this future? What are the things that I don't like about this growth scenario? And then we revert back to the present and say, how can I build towards the desirable future and how can I build away from the undesirable future? Next scenario might be the constraint scenario. So like the growth scenario, it begins with more and more people using more and more AI to do more and more things until we hit a ceiling.

It levels off. That growth scenario sort of flattens out and it's no longer more and more people doing more and more tools with more and more things with more and more tools. And then it's sort of, we're no longer adding people, tools, applications.

What would cause that? What would need to be true in the world for this explosive growth that we're seeing in AI and GovTech and a lot of other places to level off? Maybe it's a policy decision. Somebody comes out and says, "You can use AI here, but not there. We need to stop doing AI here. Maybe we reach some sort of physical limitation. No bird weighs more than 50 pounds. Maybe no AI can be more than whatever." We haven't uncovered what those limitations are yet, but we could imagine some sort of limitation gets established. Now we can put our growth scenario and our constraint scenario side by side. Imagine China has a growth scenario while the US has a constrained scenario. Flip it around. The US has a growth scenario while China has a constrained scenario. And in both of these cases, what do we like about these scenarios?

How can we build towards that desirable future? What do we not like? What makes us nervous? What's unappealing about either of these scenarios or these combinations of scenarios? How do we build away from those? How do we put in things to prevent the negative futures to create the positive, desirable futures? So you can see how these scenarios can kind of blend and combine. The third scenario we're talking about is the collapse scenario. More and more people using more and more AI. Say it with me everybody. More and more people using more and more AI to do more and more things until it doesn't keep going, it doesn't level off, the bottom drops out. We don't hit a ceiling. We're heading to the basement.

What might cause that? Again, I'm not predicting that this will happen. I'm not saying I hope it does happen. I'm saying we could imagine a future where that could happen. So would, could, should, this is in the could category. Maybe hackers come in and figure out a way to take down the data centers and just brick the data centers. Maybe there's a fundamental security flaw where we're like, oh my gosh, we didn't know that was going to happen and it all goes away. Whether it's through policy or through physical limitations or through nefarious actors, something could happen and you can make a variety of these collapse scenarios and then say, well, what do I like about this scenario? What are the good things about this collapse scenario? How could I build towards the positive elements of a collapse scenario? Because maybe it's a collapse scenario for somebody else or maybe there's good things on our side for a collapse scenario.

And then what are the things that we don't like about this scenario? How can we build away from it? Like Chanute's questions, who's on the right path, who's on the dead end path? We apply that same sort of mindset to these scenarios. The last one is the transformation scenario. More and more people using more and more AIs to do more and more things until something fundamentally changes. It's not the continuation of the previous growth. We haven't hit a ceiling. The bottom didn't drop out. It's just different. Maybe the people end up being out of the loop. Maybe the people are using it in a new and unexpected way. Maybe the bots are talking to bots in new ways. There's a whole range, and we've probably heard some of them yesterday and we'll hear more today about what this transformation might look like. Same questions before, how do we build towards the desirable?

How do we build away from the undesirable there? Now you are all experts in strategic foresight and futures thinking and you all are ready to go build scenarios.

Part three. Let's talk about the present. The fundamental building block of these scenarios that I just described, the fundamental building block is something called signals, like the term scenario, a signal sort of a term of art. A signal describes a present, observable, concrete thing that is happening right now that you can see. Signals tend to be small, they tend to be new, tend to be recent or emergent. They tend to be happening here but not there. And so as you gather a variety of signals... huh... this organization or this person is doing something interesting with this AI and this application, that's unusual. I haven't seen that before. What if more of that happened and we put this signal together with that signal and we build out our scenarios. So the signals are the fundamental building blocks of the scenarios. Good news here at Prodacity, this is a signal rich environment.

You are in a place where there's just full of these little signals if you're paying attention. So I love that they handed out these notebooks, these workbooks. So your homework, your assignment here is to collect some of the signals, to make some observations, to document them like Chanute did with his Progress in Flying Machines book, to put them together to build these scenarios so that you can imagine a range of possible futures and then make good decisions in the present to build towards the desirable future, build away from the undesirable future. Now I say this is a great place to collect signals. This is also not the only place to collect signals. There's a great paper titled 10 Principles for Thinking About the Future from the US Forestry Service. I believe the link for this is going to be shared in the chat or somewhere. And my favorite line in the whole paper, one of the best ways to anticipate change in your sector is to spend time outside of it, which is why I picked a paper from the US Forestry Service. Anybody from the Forestry Service here today? No? All right. Yeah, it's not my domain either, but when we spend time outside of our domain, we get exposed to new ideas, new principles, new signals, new practices, and things that may look obvious or ordinary or typical there might be new over here. So spending time outside of your domain, great way to find some really interesting, unique, useful signals. So the two word phrase, the two word practice that I'm recommending for when we talk about the future is just this, collect signals. So the moment we've all been waiting for, when we put these three things together from the past, study failure.

Two words, study failure. Chanute gave us a great model of what exactly that looks like from the present, building scenarios. I gave you examples of four archetypical scenarios that you could build. These four scenarios are not the only four, but you can even combine them to make a wide variety. And how do you build those scenarios? By collecting signals. So when you put the three of them together. Here we go. You put the three of them together, these are the building blocks to really help you navigate unchartered territory. Thank you. Yeah. As I was thinking about juggling in front of an audience today, I was a little bit nervous about something that something might happen while I was juggling on stage. Unfortunately, it didn't. I was afraid I might accidentally drop one. Actually, what I was afraid of, not that I would accidentally drop one, I was afraid I would forget to drop one because if I forget to drop one, then I would miss the opportunity to say study failure, right? So this is what you all remember, study failure. But the other thing that dropping a pin on stage helps me point out, failure's never the end of the story. Failure is never the whole story. Failure is only part of the story. And when we don't acknowledge our failures, we end up juggling two, which is not as cool and I'm not fooling anybody by pretending like I didn't drop that one, right?

So studying failure involves admitting failure, involves surviving failure, and not all the guys that we heard about from Chanute's book survived their failures. So don't fail that way.

All right, so that's all I'm going to say about failure. I do want to give you a little bit of recommended reading. So Jane McGonigal, I'm going to go left to right. Jane McGonigal wrote this amazing book called Imaginable. It's all about futures thinking. This is sort of my introduction to futures thinking as a practice, as a discipline. I love the book so much. It was the best book I read in 2022. I love the book so much that I went onto Coursera and took her five online courses to get certified as a futures thinking guy. Her book is great. The course is terrific. If you're going to read one of the books on the screen here, pick hers. Progress in Flying Machines, like I mentioned, Octave Chanute's book from 1894, available as a free PDF. It's nerdy. It's full of diagrams and amazing illustrations.

It's funny in some places. At one point, the humor's dry. So we're talking an engineer from the late 1800s, so it's dry humor. But as an example, at one point he described somebody as making short downward flights assisted by gravity. I think short downward flights assisted by gravity is just a fancy way of saying falling. So pay attention. Watch for the humor in his book. Then if you want a more modern take on that, my book Lift is about modern innovation lessons from aviation pioneers prior to the rights. There's about 20 copies of the book that are available for free. I'll be in the back signing them and handing them out, first come, first serve. So come meet me back there. And I'm going to leave you with one final assignment. So far I gave you three two word phrases, which were study failure, build scenarios, collect signals, and I'm going to leave you with one final assignment, it is a three word phrase and that is keep in touch.

Thank you very much everybody.

‍