When I started dating my first serious boyfriend when I was 17, I spent a lot of time thinking about thoughtfulness. By thoughtfulness, I mean, “How do I think of someone other than myself when I never have before?” Teenagers tend to be a selfish bunch – it’s a milestone when you begin expanding your psychological center of gravity beyond yourself. It was a strange question to me – in essence, I was wondering how you make something out of nothing.
Fast-forward a significant number of years, and I’ve learned since that I was asking the wrong question. I’ve taken a few courses in physics and watched the Sound of Music myriad times, and discovered that (virtually) nothing comes from nothing. The raw materials – physical and intangible – must be on hand to create something of newness. It’s what you do with them that make the outcomes new.
There are often infinite permutations, but with each action-commitment-choice made in the context of finite resources, you narrow down the pool and coalesce upon a few that shine with possibility. Sometimes the winnowing happens in single brilliant leap – from the many to the one.
But while the mechanics may be different, the optimization is the same – (1) you start as wide as possible, (2) you narrow down as best as possible, and (3) you do it as fast as possible. The genius can do #2 pretty well and #3 at lightning speed but can’t ever go as wide as, say, a billion people. These billion people – let’s call them, for the sake of simplicity, the gargantuar – encompass more permutations within their ranks, but face coordination challenges in narrowing down the pool and doing it as fast as possible. Freedom helps with starting wide but constraints help with winnowing down. Like the classic good/fast/cheap tradeoff, the three variables above often compete with each other.
In a way it’s good that they compete because that means there may be a right answer here – or more precisely, an approximately right set of answers for each problem being solved. (At least I think it’s good – if they didn’t compete, it’d be a quite boring rat race instead of an interesting strategic problem.) What is the right, in game-speak, “team build”? Two key questions (in no particular order): how many people and what sorts of people? And then to throw a wrench in, a secondary question: how do you shape the environment around those people and equip them, to help them optimize #1, #2 and #3 above?
One problem is that each problem is different. Some are messy, others straightforward. Some imply obvious tests and an evolutionary path, others have no definition except a desired outcome. Some are wide in scope, others focused. Some are technical, others intuitive. For some, relevant information is distributed across a multitude of individuals; for others, concentrated in a few. It can’t possibly be the same answer on the right process to address each of these problems. Defining the problem and its landscape must come first.
Here’s a question: Why does there need to be a “problem”, per se? Can’t innovation happen without a problem, blue-sky? Actually, no. Innovation is not just new. It is new and better. And better is about people – we define what is better. Is faster better? Is cheaper better? Is prettier better? It is – and it only is – if we say so. The gap between here and the “there” we want to go is the problem.
So assume that we have our problem. How do you solve it? You can take a good existing answer and apply it. Perfectly valid and often quite efficient. Or you can innovate – solve the problem with something new and better.
But how? Is it all custom…..or are there patterns to be found?
To be continued….
Fast-forward a significant number of years, and I’ve learned since that I was asking the wrong question. I’ve taken a few courses in physics and watched the Sound of Music myriad times, and discovered that (virtually) nothing comes from nothing. The raw materials – physical and intangible – must be on hand to create something of newness. It’s what you do with them that make the outcomes new.
There are often infinite permutations, but with each action-commitment-choice made in the context of finite resources, you narrow down the pool and coalesce upon a few that shine with possibility. Sometimes the winnowing happens in single brilliant leap – from the many to the one.
But while the mechanics may be different, the optimization is the same – (1) you start as wide as possible, (2) you narrow down as best as possible, and (3) you do it as fast as possible. The genius can do #2 pretty well and #3 at lightning speed but can’t ever go as wide as, say, a billion people. These billion people – let’s call them, for the sake of simplicity, the gargantuar – encompass more permutations within their ranks, but face coordination challenges in narrowing down the pool and doing it as fast as possible. Freedom helps with starting wide but constraints help with winnowing down. Like the classic good/fast/cheap tradeoff, the three variables above often compete with each other.
In a way it’s good that they compete because that means there may be a right answer here – or more precisely, an approximately right set of answers for each problem being solved. (At least I think it’s good – if they didn’t compete, it’d be a quite boring rat race instead of an interesting strategic problem.) What is the right, in game-speak, “team build”? Two key questions (in no particular order): how many people and what sorts of people? And then to throw a wrench in, a secondary question: how do you shape the environment around those people and equip them, to help them optimize #1, #2 and #3 above?
One problem is that each problem is different. Some are messy, others straightforward. Some imply obvious tests and an evolutionary path, others have no definition except a desired outcome. Some are wide in scope, others focused. Some are technical, others intuitive. For some, relevant information is distributed across a multitude of individuals; for others, concentrated in a few. It can’t possibly be the same answer on the right process to address each of these problems. Defining the problem and its landscape must come first.
Here’s a question: Why does there need to be a “problem”, per se? Can’t innovation happen without a problem, blue-sky? Actually, no. Innovation is not just new. It is new and better. And better is about people – we define what is better. Is faster better? Is cheaper better? Is prettier better? It is – and it only is – if we say so. The gap between here and the “there” we want to go is the problem.
So assume that we have our problem. How do you solve it? You can take a good existing answer and apply it. Perfectly valid and often quite efficient. Or you can innovate – solve the problem with something new and better.
But how? Is it all custom…..or are there patterns to be found?
To be continued….
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