Blitzscaling: Network Effects

By Chris Yeh, co-author of Blitzscaling and Founding Partner along with Jeremiah Owyang, General Partner.

We often see confusion when it comes to network effects and virality. For example, some people think virality is a network effect because it taps into users’ networks. This post should clarify how we at Blitzscaling Ventures see network effects, and why they are so important to our investment decisions.

Network effects happen when each new user added makes the product more valuable for the prior users—this creates momentum, builds loyalty, and helps companies grow and hold onto market share. For example, each new Facebook user makes the network slightly more valuable for all the existing users, who now have one more person they can reach.

As the number of users grows, the value of the network to its individual users grows. This has two key effects:

First, the value of the company scales according to the value it delivers to its users. When increasing the number of users also increases the value per user, company value grows explosively.

Second, as the network value of the product increases, it eventually exceeds the base value of the product, which means that no matter how great a product a new entrant in the market offers, it can’t compete with the network value of the market leader.

At Blitzscaling Ventures, we use a scorecard to evaluate whether a startup has the potential to win a winner-take-most market. This framework helps us identify companies that can scale rapidly and defensibly. Two of the key growth levers we assess are virality/distribution and network effects (the subject of this post). Both are essential to outpacing competitors.

We look for several distinct types of network effects:

Direct Network Effects: The value of a product increases as more people use it. The classic telephone network diagram (such as the graphic embedded in this post) is the perfect visual metaphor. Think of WhatsApp or Slack—each new user means more connection points, which makes the platform more useful to existing users.
Data Network Effects: More users generate more data, which improves the product for everyone. For example, OpenAI’s models like ChatGPT become more capable as they interact with more users, generating feedback and insights that enhance future iterations. In B2B, fine-tuning and usage across enterprise clients further strengthen model relevance and accuracy.
Indirect Network Effects: Growth in one user group enhances the experience for another. For instance, more electric vehicle (EV) owners increase the demand for charging stations, which in turn makes EV ownership more attractive—creating a reinforcing loop between drivers and infrastructure providers.
Multiple-Sided Marketplace Network Effects: In marketplaces, more buyers attract more sellers—and vice versa. Examples include Uber, Airbnb, and eBay. Advanced models feature multi-sided marketplaces with three or more participant groups. For example, Google’s Android Play Store brings together developers, users, and device manufacturers—each group reinforces the value for the others.
B2B Network Effects: When businesses adopt and integrate a platform, it becomes more valuable for others in the ecosystem. Consider Amazon Web Services and Stripe—wider adoption leads to deeper integration, more data connections, and richer features.
Learning Effects: AI platforms that learn and adapt over time become more powerful as they interact with more users and data. Think Claude by Anthropic or Waymo—both improve their systems through real-world interactions, user feedback, and continuous model refinement.
Emerging Network Effects: There are other forms—and new ones may emerge—which we’ll continue to evaluate. Sometimes it’s hard to categorize, but we know it when we see it, and you'll tell us!

Some of the most successful startups—like Airbnb, Slack, and Figma—combine multiple types of network effects for a powerful compounding advantage. For example, Airbnb benefits from two-sided network effects (hosts and guests), data network effects (recommendations improve with usage), and indirect effects (an entire industry of Airbnb-enablers such as property management firms and cleaning services has sprung up).

When network effects are paired with virality, they create a flywheel: virality attracts new users or customers, and network effects keep them engaged. This combination helps a company attract and win most of their market.

We are far from the only folks who care about network effects. If you are interested in diving deeper on the topic, we recommend the strong body of work on network effects from our friends at NFX, a fellow investor. Network effects are one of the most powerful growth engines for technology startups. Not only does the value increase with every new user, but it also creates incredible stickiness—drawing customers back in, almost like the product has its own inescapable gravity. This stickiness also makes it harder for competitors to attract and retain customers.

Related: