Long-time readers may know that while the focus of this publication is primarily on the human side of the business, I sometimes dabble in strategy and other “business fundamentals”.
This topic has been of particular interest to me over the years since there seems to be a large “knowing vs. doing” gap around it. While a moat (we’ll get to the definition in a second) is essential to the long-term longevity of any business, it seems to be an afterthought at best in most startups. “What moat are we creating?” does not seem to drive any of the critical business decisions that most startups are making.
I started exploring this topic in more detail in 2014, musing on Fred Wilson’s Dentist office software parable, and later discussing Ben Thompson’s analysis of Uber’s strategy, sharing my own summary on Network Effects, and collaborating with Eric Jorgenson on Evergreen’s last(?) edition. Throughout the years, they’ve been a few other resources that almost made the cut. Nfx’s Network Effects Manual probably came closest.
But this recent post from Jerry Neumann really knocks it out of the park:
One of the things that made exploring this issue challenging in the past was a notable overlap in the distinctions between some key terms: barriers to entry, network effects, marketplaces, economies-of-scale — all seem to be having a lot in common.
Neumann reconciles this issue in the opening paragraph:
Value is created through innovation, but how much of that value accrues to the innovator depends partly on how quickly their competitors imitate the innovation. Innovators must deter competition to get some of the value they created. These ways of deterring competition are called, in various contexts, barriers to entry, sustainable competitive advantages, or, colloquially, moats.
Yes! Moats are barriers to imitating innovation due to structural causes, as opposed to talent, vision and the likes.
The rest of the post lays out a comprehensive taxonomy of moats organized by the 4 key structural causes that generate them:
- State Granted (patents, tariffs, regulation, etc.)
- Special Know-how (tacit knowledge, customer insights, etc.)
- Scale (network effects, sunk costs, willingness to experiment, etc.)
- System Rigidity (business model innovation, brand, complementary assets, etc.)
In addition to providing a more detailed explanation of each category and providing concrete examples for the application of the moat, Neumann also looks at it specifically through the lens of a startup/early-stage company, assessing the viability of building that moat as an initial strategy. Most of that is also summarized in the final paragraphs:
Some startups have a moat they start with. These moats are generally fungible: they have the same or greater value if they are sold to an existing company as they would if they were incorporated into a new company… Other startups develop moats over time. No company can have a moat from returns to scale, for instance, before they have scale. No company can generate collective tacit knowledge in their organization until they have an organization. And building links from the product into the surrounding system takes time and, usually, a working product… Startups can avoid the competition of better-resourced incumbents for some period of time by using system rigidity against them through disruptive innovation or value chain innovation… [but] developing a moat based on system rigidity also takes time.
But it’s the next logical leap that Neumann takes that is truly mind-blowing:
Uncertainty can be seen everywhere in the startup process: in the people, in the technology, in the product, and in the market. This analysis shows something more interesting though: uncertainty is not just a nuisance startup founders can’t avoid, it is an integral part of what allows startups to be successful. Startups that aim to create value can’t have a moat when they begin, uncertainty is what protects them from competition until a proper moat can be built. Uncertainty becomes their moat.