In a recent article, it was fun to read how Greylock’s most recent partner, Josh McFarland pitched to Jeff Bezos, who apparently said, “Most ideas are worth what you pay for them,” but then asked to keep McFarland’s page. I’d recently worked closely with Tellapart, and had in fact spoken with McFarland at GHC16 only a couple of months ago. Back then, I’d honestly wondered how long he’d stay at Twitter, so connecting the dots was fun. But I digress.
Bezos probably sees dozens of new ideas everyday, who knows. A few of them make it to real products. However, the value that he has created goes far beyond either ideas or products. He has created a machine that converts ideas into products. The accumulated knowledge of how to do this is the greatest value of all, and that’s what I’ve been trying to unpack for myself. Not that I’m going to do a fair or even a complete job, but to get started I want to draw some parallels with a book I recently read on How Information Grows.
Agreeing that persistence and accumulated knowhow enable the conversion of an idea to a new product, we can further chip away at what drives or limits the creation of this knowhow or knowledge. Knowledge is limited by an individual’s capacity to learn, and learning that is experiential requires both time and other people with relevant experience. Skills that depend on intellectual capital, craft or judgment are honed over years through education, training, and experience. Knowledge intensive jobs such as scientific research, service design, and general design work require people learning from other people with relevant experience. So, limited by individual capacity, we create networks of people to increase our capacity to learn. But what drives people’s ability to form effective networks?
1. Informal connections, distributed experiments
Silicon Valley’s dense social networks encourage entrepreneurship and learning. Companies compete intensely while at the same time learning from one another about changing markets and technologies with people intermingling to develop knowhow. Boundaries between teams, companies, local institutions, and universities are fairly porous. Amazon has created similar porous boundaries between scores of businesses that learn from each other, and from its executives who have learnt from experience. Hundreds of experiments are carried out by individual teams, and anyone can reach any other person in the company to get advice, mentorship, and sponsorship.
2. Combining diversity
The knowledge and skill of a soccer team hinge on its diversity since strikers and goalies differ in the individual knowhow they possess. Players contribute to the team by adding knowledge and knowhow that is not redundant with others. This diversity allows the team to perform actions that cannot be performed by single individuals like winning a soccer game or playing in an orchestra. This division of knowledge and knowhow, not just labor, is what endows networks of people with fantastic capacities such as those required by a soccer team to win the Champion’s League. Amazon combines the knowhow of running retail and digital businesses for consumers as well as running highly reliable services for enterprises. These businesses add to each other in visible ways to grow the flywheel of human and invested capital. Silicon Valley is far more diverse in the people it employs as well as the products and services it produces, but it lacks coordination.
Former coach of Barcelona, current coach of Bayern Munich, Josep Guariola was asked by a student at the MIT Media Lab: “Pep, if we built a team of robots, would you come and coach it?” He replied, “The main challenge of coaching a team is not figuring out the game plan but getting the game plan into the heads of players. Since in the case of robots, I do not see that as a challenge, I kindly decline the offer.” Plans at Amazon are six page narratives that are reviewed at every level. The idea is to provide in-depth knowhow that anyone who reads the plan can grasp so they can ask questions to tease out the group’s experiential learning. These plans discuss the what as well as the why.
I usually imagine culture to be an intrinsic characteristic of a network, but at Amazon its “leadership principles” could probably represent an executive team member in their own right. These principles are the only instance I’ve seen of a company scaling its culture, and providing a common language for all participants to articulate individual behaviors and their impact literally in short hand. It’s a meta language that allows us to assess people in addition to other quantitative measures.
So, if Amazon were just a piece of land, I wonder if South Lake Union might have been Washington’s Silicon Valley. Reversing the analogy, and maybe stretching it beyond safe consumption, if Silicon Valley were a company and you could have stock in Silicon Valley, which would you pick?