How To Get Your First Users...

Most people are not early adopters; they rarely use products they were among the first 10 users of. Nearly all users avoid being a startup’s first paying customer. However, every successful product finds initial users. The earliest product versions only need to survive contact with a small group willing to try it, focusing on evolvability rather than final form. This initial version is a "minimum evolvable product" (MEP), poised for adaptation.

There exist individuals who enjoy being early adopters, such as Gustaf from Airbnb who tried new startup products for his company. Others have critical issues that drive them to try any new solution. For example, a team needing to ship an inference API quickly paid a startup within three days because it solved their immediate billing and public endpoint issues, becoming their first customer. Their problem's urgency outweighed concerns about the startup's size or reputation.

The lesson for user acquisition is that it is fundamentally a search problem, not a persuasion problem. The goal is to locate individuals like Gustaf (early adopters) or those with urgent problems that the product can solve. This approach implies several counterintuitive strategies for startups.

Charge real money from the outset. Early adopters and users with urgent problems are typically not price-sensitive. The primary goal at this stage is not revenue maximization but rather feedback acquisition. Paying customers provide significantly sharper feedback compared to free users, as angry paying customers are more likely to offer critical insights. This feedback is essential for product iteration.

Employ targeted personal outreach instead of broad advertising. Traditional methods like billboards are ineffective for reaching the niche group of early adopters. More precise methods, such as targeted cold emails or direct engagement, are far more likely to connect with these specific individuals. This direct approach fosters personal relationships that are crucial in early product development.

Launch early and frequently. Y Combinator has long advocated for early launches. In initial stages, specific knowledge about early users is limited. Launching early creates a broad surface area for potential users to discover the product. This approach allows for quicker validation and identification of key user segments. This strategy prioritizes rapid deployment over perfection.

Study early users intensely, much like an anthropologist observing a hidden civilization. Understand their decision-making processes, the reasons behind their choice to trust a nascent product, and their specific needs and desires. This deep understanding informs product development and strategic adjustments.

Experiment rapidly and embrace churn. Startups should run constant experiments across all aspects of the product, including pricing, landing pages, onboarding processes, and features. While engaging with early users to cultivate product loyalty is important, do not fear churn. If a user is annoyed, it is often fixable through personal interaction. If they churn, there are many other potential users who have not yet discovered the product. This agility is an advantage for startups over larger companies; bad experiments rarely attract public attention, and the focus remains on overcoming irrelevance.

These initial interactions significantly shape the target audience. For instance, most individuals have low personal software spending (e.g., $150/month), whereas corporate accounts often spend more on single tools. In the AI era, this disparity is critical because advertising revenues may not cover high AI operational costs for consumer apps. Consequently, many AI founders target prosumers, businesses, or high-value professionals like doctors, prioritizing higher advertising value and larger budgets. Early users not only provide feedback but actively steer the product's evolutionary trajectory.

Consider a startup as a phylogenetic tree, beginning as a root node or amoeba and evolving into complex multicellular organisms like humans or dogs. Most mature products on the market have undergone this evolutionary process, starting from a basic form (amoeba) to a refined product with millions of users and clear value propositions. Early startups are amoeba-like, possessing only fundamental functions necessary for initial market exposure.

Founders conduct an evolutionary search through potential future directions. Tesla's Roadster serves as a case study: a high-margin product funding capital expenditure for later models (Model S, Model 3, Model Y). The Roadster also served to find early adopters willing to purchase an expensive, impractical, and visually distinctive car that lacked features present in mature vehicles. This revealed a crucial insight: product evolution is path-dependent on early adopter preferences.

The Tesla Model Y, a mass-market vehicle, boasts rapid acceleration (0 to 60 faster than a Lamborghini) and advanced tech but inferior suspension and comfort compared to a Toyota. This design reflects early adopters' priorities—tech and acceleration over comfort. A mass-market vehicle designed in a vacuum would likely not prioritize a 0-60 time under 3 seconds. The product's current form is an outcome of the search algorithm Tesla ran. Had early adopters preferred a slow, luxurious vehicle, Tesla's product line would be drastically different today. This illustrates why the initial product should be a minimum evolvable product, capable of responding to market pressures and adapting based on early user feedback.