For network effects to occur, the product or service has to be highly relevant to its users. When relevance fades, network effects quickly unravel.
Michael Kao likes to point out that the sword of inelastic supply cuts both ways. By the same token, the sword of network effects cuts both ways: if the network shrinks, the value destruction is disprortionate.
Thank you for sharing your thoughts on this, Todd.
To play devil’s advocate: The example of Cincinnati can be turned around to show that even though the main reason for the (population) growth diminished due to competitors (other cities) with clear advantages (location).
Still the city didn’t seize to exist and is today 3x the size of the boom times.
Myspace at its peak had 100 million users. Last figures I found from 2019 there were still 7 million users active.
To your example of ODFL vs. Yellow. I would argue the utility of a network is not purely a function of number of nodes. Just as Facebooks utility diminished for users with less people posting stuff, even though the absolut number of users kept growing.
Network effects are not infallible, but I would argue the competitors couldn’t compete without network effects themselves. The competitors were able to compete based on value add + their own network effect. Where the real size of the network and the extrapolation of current trends come into effect.
Network effects come in shades of different strengths and with/without exponential, linear, or asymptotic value add properties. Physical networks being anchored in the real world with substitution costs being more permanent than purely digital.
The original network effect of providing additional benefits (opportunities) to customers can still exist but is outweighed by other factors. In case of Myspace multi-tenant is coming to mind. In case of cities, I would argue the benefits are asymptotic, where the additional benefits diminish after a certain size. In 19th century this size was probably even smaller than today due to mobility and communication limits.
I really like the differentiation between types of network effects done here: https://www.nfx.com/post/network-effects-manual
Reminds me of population growth in the Washington DC area in the 70s when the government was fueling so much job growth and the rest of the country was relatively stagnant. Once jobs picked up again, other states saw more growth and people started moving again.