We take ideas from other people, from people we've learned learned from, from people we run into in the coffee shop, and we stitch them together into new forms and we create something new. That's really where innovation happens. And that means means that we have to change some of our models of what innovation and deep thinking really looks like, right. I mean, mean, this is one vision vision of it. Another Another is Newton and the apple, when Newton was at Cambridge. This is a statue from Oxford. You know, you're sitting there thinking a deep thought, and the apple falls from the tree, and you have the theory of gravity. gravity. In fact, the spaces that have historically historically led to led to innovation tend to look like this, right. This is Hogarth's famous famous painting of a kind of political dinner at a tavern, but this is what the coffee shops looked like back then. This is the kind of chaotic chaotic environment where ideas were likely to come together, where people were likely to have new, interesting, unpredictable collisions people from different backgrounds. So, if we're trying to build organizations that are more innovative, innovative, we have to build spaces that strangely strangely enough look a little bit more like this. This is what your office should look like, is part of my message here.
And one of the problems with this is that people are actually when you research this field people are notoriously notoriously unreliable, unreliable, when they actually kind of self report on where they have their own good ideas, or their history of their best ideas. And a few years ago, a wonderful researcher named Kevin Dunbar decided to go around and basically basically does the Big Brother approach approach to figuring out where good ideas come from. He went to a bunch of science labs around the world and videotaped everyone as they were doing every little bit of their job. So when they were sitting in front of the microscope, when they were talking to their colleague at the water cooler, and all these things. And he recorded all of these conversations and tried to figure out where the most important ideas, where they happened. And when we think about the classic image of the scientist in the lab, we have this image you know, they're pouring over the microscope, and they see something in the tissue sample. And "oh, eureka," they've got the idea.
What happened actually when Dunbar kind of looked at the tape is that, in fact, almost all of the important breakthrough breakthrough ideas did not happen alone in the lab, in front of the microscope. They happened at the conference table at the weekly lab meeting, when everybody got together and shared their kind of latest data data and findings, oftentimes when people shared the mistakes they were having, the error, the noise in the signal they were discovering. And something about that environment and I've started calling it the "liquid network," where you have lots of different ideas that are together, different backgrounds, different interests, jostling jostling with each other, bouncing off each other that environment is, in fact, the environment that leads to leads to innovation.
The other problem that people have is they like to condense condense their stories of innovation down to kind of shorter time frames. So they want to tell the story of the "eureka!" moment. They want to say, "There I was, I was standing standing there and I had it all suddenly clear in my head." But in fact, if you go back go back and look at the historical historical record, it turns out turns out that a lot of important ideas have very long incubation periods I call this the "slow hunch." We've heard a lot recently about hunch and instinct instinct and blink blink like sudden moments of clarity, but in fact, a lot of great ideas linger linger on, sometimes for decades, in the back of people's minds. They have a feeling that there's an interesting problem, but they don't quite quite have the tools yet to discover them. They spend all this time working on working on certain certain problems, but there's another another thing lingering lingering there that they're interested in interested in but they can't quite quite solve.
Darwin is a great example of this. Darwin himself, in his autobiography, tells the story of coming up with coming up with the idea for natural selection as a classic "eureka!" moment. He's in his study, it's October of 1838, and he's reading Malthus, actually, on population. And all of a sudden, the basic algorithm algorithm of natural selection kind of pops into his head and he says, "Ah, at last, I had a theory with which to work." That's in his autobiography. About a decade or two ago, a wonderful scholar named Howard Gruber went back went back and looked at Darwin's notebooks from this period. And Darwin kept these copious copious notebooks where he wrote down wrote down every little idea he had, every little hunch. And what Gruber found was that Darwin had the full theory of natural selection for months and months and months before he had his alleged alleged epiphany, reading Malthus in October of 1838. There are passages where you can read it, and you think you're reading from a Darwin textbook, from the period before he has this epiphany. And so what you realize realize is that Darwin, in a sense, had the idea, he had the concept, but was unable unable of fully thinking it yet. And that is actually how great ideas often often happen; they fade fade into view over long periods of time