Collecting user data is a competitive disadvantage

Warren Buffet is famous for identifying the need for businesses to have "moats" and "walls" around their profit-centers to keep competitors out, and data-centric companies often cite their massive collections of user-data as "moats" that benefit from "network effects" to make their businesses good investments.

In a smart, eye-opening essay, Martin Casado and Peter Lauten from the VC firm Andreesen Horowitz dismantle the idea that data benefits from "network effects" and that it presents any kind of "moat" to protect businesses: instead, the VCs demonstrate how collecting data gets more expensive, and less useful, over time.

To understand why, think of Netflix's data-collection, performed in service to its famous recommendation engine, which suggests programs you might enjoy based on the preferences of people who are similar to you. When Netflix is starting out, it needs to develop a "minimum viable corpus" in order to produce recommendations, but once that data is in place, new data produces diminishing returns in recommendations. Going from 100 to 1,000,000 users allows Netflix to dramatically improve its recommendations, but going from 1,000,000 to 1,000,100 (or even 2,000,000) produces very little new benefit.

Meanwhile, adding in all that data is expensive: first, because once everyone who already understands why they might subscribe to Netflix is a customer, Netflix has a much harder job of convincing the remaining population that it's worth their while to join up (their "cost of user-acquisition" goes up). Second, the computational costs of incorporating new data into a prediction model don't necessarily go down with volume, so the cost of recomputing the model when you add your 1,000,001st user isn't necessarily cheaper than when you add your 101st user (it might even be more expensive), and since the new user adds less value to the model than previous users did, the real costs of new users' data (relative to the benefits) are constantly going up.