In 2016, I had a debate with a recently graduated chemical engineer (let’s call him Alan). Alan was enraptured with cryptocurrencies and had just started consulting with a well known team in Ethereum.
Our debate revolved around the future of auction sites. At the time, OpenBazaar — a decentralized auction site with similarities to eBay — was making waves with its no fee model and large venture capital fundraise.
Alan was adamant that OpenBazaar’s no fee model would disrupt eBay: consumers preferred lower fees and would switch over. To illustrate his point, he asked for some paper and drew an image from his chemical engineering days:
He asked me what would happen, if this was a chemical in water?
“Diffuse,” I responded.
“Exactly. Now, think of these bubbles as users.”
He then added labels to his illustration, now with eBay on the left, Open Bazaar on the right:
“This is the current state of the world.” Alan drew another image:
“This is what happens when a low fee model enters the market. The low fee entrant attracts the bubbles.”
“After sufficient time, the users all migrate over.”
For most digital business observers, this is a flawed comparison. Network effects in double-sided marketplaces are hard to break. For any user, the reduced fees are overwhelmed by the choice present in existing marketplaces. Service providers (Uber drivers, eBay merchants, Airbnb hosts) face the same calculus, leading to a coordination problem.1
This is precisely the reason that businesses like Uber, eBay, and Airbnb command such high valuations, relative to their short-term profitability. In economic terms, these businesses have a moat that is not easily scaled. At minimal, OpenBazaar would have to have a team hell bent on user acquisition, not simply changes to business model or better technology to compete head on with eBay.
More interesting to me than the debate about this topic, was a broader realization: It can be easy to take frameworks that we’ve learnt and apply them in contexts where they don’t make much sense. Every profession will have these blindspots, based on the training that they receive.