Much has been written about Network Effects and Platform Potential and New Monopolies in consumer and/or social products. It’s easy to understand how a marketplace like Airbnb or a messenger like Slack build defensible moats through network effects. It’s even fairly obvious how an infrastructure platform product like Segment can build self-reinforcing momentum that takes profit share away from their analytics partners.
Elsewhere in SaaS and infrastructure, it’s less clear which layers and products in the tech stack will be the ones that profit margin naturally flows to, and which layers will become commodities.
Opinion (and bets) within the sophisticated venture and startup community is always divided. In fact this question is one of the most central to venture investors: given equivalent funding and team quality, which products, approaches and categories have an unfair structural and strategic advantage?
I like to think of this question as analogous to core value investing concepts like Warren Buffet’s quip about preferring businesses that even ‘your idiot nephew could run’
For example, a very smart Series A investor I know told me a couple of years ago that he had a strong thesis that test automation services would tend towards commodity, but that margin and pricing power would pool around server monitoring services.
Smart investors in test-automation companies have a different thesis.
One reason that test automation products may tend toward commodity relative to monitoring products is that a series of passing test runs and successful code integrations does not generate the same uniquely valuable time-series data asset that server logs do.
As an investor, I follow analytics and BI products pretty closely. An interesting conversation that I have been having with experts recently is the relation between Amazon AWS products like Redshift and Quicksight and the current generation of venture-backed BI companies like Periscope, Chartio, Looker, and Domo. Many of these products pull customer data into Amazon Redshift and then build visualizations and dashboards from there. Now Amazon offers a similar native product: Quicksight. The platform and associated pricing power risk to these startups is obvious. The alternative thesis is that the Amazon product will always need to offer 100% configurability and complexity so won’t fulfill the fundamental promise of the startup BI products to democratize data analysis down from engineers to line business analysts and managers.
As in the first example, there are very smart venture investors on both sides of this bet.
The core value-investing literature on businesses with defensible moats and lasting franchise value with margins of safety from Buffet, Munger, Klarman, et al. provides amazing insight for venture investors operating in what appears superficially to be a completely different asset class. For further reading, some of the smartest writing on technology investing like Bill Gurley’s Above The Crowd and Tren Griffin’s 25IQ really connect the dots between the two disciplines.
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