Network Effect
A network effect occurs when a product becomes more valuable to each individual user the more other people also use it. It explains why, with messengers, marketplaces, and platforms, a market is often dominated by just a few large providers.
Most things people buy are useful independent of other buyers. A bicycle rides just as well whether no one else owns one or half the city does. With some products, this is different. A messenger app you use to send messages is worthless as long as you can’t reach anyone there. With every additional person who uses it, its value rises for everyone else. This exact relationship is called a network effect.
Why this often leads to monopolies
Network effects create a feedback loop. More users make the product more attractive, which attracts even more new users. Past a certain point, this cycle runs almost by itself. The runner-up can then have a better product and still lose, because it lacks the other people.
For users, this creates a kind of lock-in. Switching to a competitor costs not only time but also contacts. Experts call this switching costs. Anyone who switches providers alone, without their circle of friends, loses exactly what they used the product for. That’s why many people stick with a service they don’t even like anymore.
For investors, a network effect is therefore one of the strongest arguments of all. It acts like a moat around the business. For the same reason, antitrust authorities take a keen interest in such markets. They examine whether a company is using its size to deliberately keep competitors out.
Direct and indirect forms
Two basic types are distinguished. In a direct network effect, users benefit immediately from one another, as with the telephone or a messenger. Every new connection increases the number of possible links. In an indirect network effect, two different groups come together. On a marketplace, many buyers attract many sellers, and many sellers in turn attract many buyers.
At the start, this is a real problem. An empty marketplace is useless to anyone, so no one comes. This startup problem is called the chicken-and-egg problem. Companies usually solve it with a lot of money: they give the service away for free, pay early providers, or fill the platform themselves with content until enough real users are there.
A common misconception is that every large company automatically has a network effect. Size alone is not enough. A supermarket with a thousand branches doesn’t get better for an individual customer just because many people shop there. What matters is that the benefit for the individual truly depends on the other users. Conversely, the effect can also tip: too many users sometimes mean spam, advertising, or overcrowded networks.
Network effects in AI products
In everyday life, the term comes up wherever people coordinate with one another. Messengers, social networks, auction platforms, operating systems with their apps, and payment services are classic examples. Programming languages follow this pattern too: the more developers use a language, the more ready-made building blocks and tutorials exist for it.
In reports about artificial intelligence, the term appears regularly. Chatbot providers argue that user feedback improves their models, which in turn brings more users. Whether this is a genuine network effect is disputed among experts. That’s because users gain nothing from the fact that others use the same chatbot, unlike with a messenger.
The effect is more clearly visible around the models themselves. When many developers build on the same provider’s interface, tools, courses, and extensions emerge. This environment is called an ecosystem. It makes switching to another provider expensive, even if that provider’s model is technically equivalent.