Network Effects: Why Value Grows With the Crowd

What this means

A single telephone is worthless. Not cheap, not limited, literally worthless, because there is nobody to call. A second telephone makes both of them useful. A millionth telephone makes every existing phone more useful than it was the day before, and the phones themselves did not change at all.

That is a network effectA situation where a product or service becomes more valuable to each user as the number of users grows.. The value sitting inside the product depends on how many other people are using the same product.

Notice how strange this is compared to most goods. A better pair of running shoes is better because of the shoe. The engineering improved. With a network-effect product, your experience improves when nothing about the product improved, because the thing you actually want from it is access to other people.

Economists usually separate two kinds. A direct network effectA network effect where users of a product gain value directly from other users of the same product. runs straight between users: a messaging app is more useful when your friends use that same app. An indirect network effectA network effect where growth in one group of users attracts a second group, which then makes the product more valuable to the first. runs through a second group: more people owning a particular game console attracts more developers to build for it, and a larger game library then attracts more console buyers.

Two cautions keep this concept honest. First, popularity alone is not a network effect. A hit song is popular, but your enjoyment of it does not increase because strangers stream it too. Ask the diagnostic question: does another user joining make the product more valuable to me? If not, it is just demand. Second, network effects can run backward. When users leave a platform, the remaining users lose value, which pushes more of them to leave. The same mechanism that builds fast can unwind fast.

Why it matters

Almost every service you open on a phone is one you chose partly because of who else was already there. You are not neutrally comparing features when you pick a messaging app. You are joining the room your friends are in, and that constraint is doing more of the deciding than any feature list.

This also explains a frustration you have probably felt: knowing a competing app is better in some measurable way and using the popular one anyway. That is not irrationality. A technically superior app with nobody on it genuinely delivers less value to you. Recognizing this makes you a sharper reader of technology news, because it explains why companies give products away free for years. They are not confused about revenue. They are buying users, since users are the product's value.

Try it

1. List every electronic service you personally used in the past week. Aim for at least twelve, and include the boring ones, such as email, a school portal, a payment app, and a document editor.
2. For each one, apply the diagnostic test in writing: if one more person joined this service tomorrow, would it become more valuable to me? Answer yes, no, or only indirectly.
3. Sort your list into three piles. Pile A is strong network effects. Pile B is no network effect, meaning the service is just as good with one user as with a billion. Pile C is contested, and you must defend why it is hard to classify.
4. For each service in Pile A, specify who has to join for the value to rise. Everyone in the world, or only your specific friends? This distinction separates global network effects from local ones, and it explains why a small group can successfully use an app almost nobody else uses.
5. Pick one service in Pile A and identify whether the effect is direct, indirect, or both. Write the chain of causation explicitly: more of group X attracts more of group Y, which attracts more of group X.
6. Take your strongest Pile B example and stress-test it. Could a company add a feature that manufactures a network effect where none exists? Describe the feature and judge honestly whether it would work or would feel bolted on.
7. Run a switching experiment on paper. Choose a Pile A service and estimate what you would personally lose by leaving it tomorrow. Separate the loss into value from the software itself and value from the people on it. The second number is the network effect, measured.
8. Find a counterexample: a service that was once dominant and lost its users. Describe the reversal in network-effect terms, tracing how departures made staying less valuable for the people who remained.
9. In a class discussion, compare Pile A lists. Where students disagree about the same service, work out whether the disagreement is about the definition or about the facts of how that service is used.

Check yourself