Finding early adopters is a search problem, not a persuasion problem, and the people you find will shape your product's entire evolutionary path.
The Early Adopter Problem
Ask yourself: how many products do you use today that you were among the first ten users of? For most people, the answer is zero. Almost no one wants to be a startup's first paying customer. Yet every great product still manages to find a few people willing to take that leap. The earliest version of your product only needs to do one thing: survive contact with a tiny group of people who might actually try it. You are not building the final form; you are building something that can evolve. When you are starting out, you do not just need a minimum viable product. You need a minimum evolvable product.
Before you get discouraged, know that there are people who love being early adopters. One of my colleagues, Gustaf, worked at Airbnb for years and enjoyed trying out products from startups and bringing them into the company. Others have such a burning issue that they are willing to give any new product a shot if it looks like it could make their life easier. For example, when my team needed to ship our first inference API, we wanted to ship it fast and did not want to deal with figuring out billing or public endpoints. Within three days, I found and paid a startup whose product solved that issue for us. I was their first customer. Their size or reputation did not matter. Our problem did, so we took a chance on them, and they delivered.
A Search, Not a Persuasion
The lesson is simple: finding your first users is more of a search problem than a persuasion problem. You are looking for the Gustafs and Ankits of the world—the ones who try new things or have a burning need that you can solve. This insight has several counterintuitive implications that you will want to pay attention to. First, charge real money early. Early adopters and people with a burning problem are rarely price sensitive. The goal here is not revenue; it is feedback. Paying customers give sharper feedback than free users ever will. You are more likely to get honest, actionable criticism from an angry customer paying a lot of money than from a nobody who is not willing to pay.
Second, use targeted personal outreach. The ways you find these people probably do not look like how you find normal users. A billboard is far less likely to reach them than a targeted cold email or a knock on their door. Third, launch early. In the early days, you do not know much about who these early users are, and you want to engineer a wide surface area for them to find you. Fourth, study your early users closely. You should be like an anthropologist who has discovered a hidden civilization. How do they make decisions? Why would they make the strange choice to trust you? You want to understand how they think and what they want.
Experiment and Embrace Churn
Finally, experiment fast and do not fear churn. You should be running constant experiments on pricing, landing pages, onboarding, features—everything. At the same time, talk to your early users and try to make them love the product. But do not stress if you lose one of them. If you annoy someone, you can usually fix it because the relationship is personal. And if they churn, that is fine too. There are plenty of others who have not even heard of you yet. This is one of the advantages startups have over big companies. When you run a bad experiment, no one writes about it. You are fighting irrelevance, not headlines.
All of this may shape who you are even building for in the beginning. Most people do not pay for a bunch of consumer apps. The average personal software spend is pretty tiny. For example, mine is about $150 a month total. Meanwhile, my corporate card has multiple tools that each cost more than that alone. In the AI era, that gap matters. Consumer apps can struggle because ads often do not cover AI costs, and subscriptions have to squeeze into an already small personal budget. Of course, many consumer companies will still be made, but this is why many AI founders choose to start by selling to prosumers or businesses or targeting users like doctors that have high advertising value.
The Phylogenetic Tree of Startups
Your early users do not just give you feedback. They end up steering how your product evolves over time. Think of a startup as a phylogenetic tree. The root nodes are amoebas, and the leaf nodes are complex multicellular organisms like humans or dogs. Almost every product you buy on the market has run this evolutionary process and morphed from an amoeba to the maturity of a human or a dog: millions of users, a refined sales pitch, and clear value. Early startups are more like amoebas. They have just the very basic functions needed to get exposed to external pressures. But from there, the founders run an evolutionary search through the tree of potential future directions.
Consider Tesla as a case study, specifically their amoeba: the Tesla Roadster. The lore about the Roadster is that Tesla needed a high-margin product to fund their capex investment to make the Model S and eventually the Model 3 and Y. That is probably true, but there is a second interpretation. Tesla was searching for early adopters. They wanted to find the people crazy enough to buy an impractical $150,000 car that did not go very far, did not fit much in it, could not publicly charge anywhere, and looked strange. Tesla's story reveals another reality: product evolution is path dependent on what the early adopters wanted.
Path Dependence and Evolution
Why does the Tesla Model Y, a mass market vehicle, have a faster 0 to 60 than a Lamborghini and better tech than a BMW, but worse suspension and comfort than a Toyota? It turns out that early adopters cared much more about tech and acceleration than comfort. Would a mass market vehicle designed in a vacuum have a 0 to 60 of under three seconds? Probably not. But it is an outcome of the search algorithm that Tesla ran. If early adopters were willing to pay $150,000 for a slow, plush vehicle, I bet Tesla's cars would look very different today.
This is the algorithm we help founders run at YC. And it is why your first version should not just be a minimum viable product. It should be a minimum evolvable product: something simple that can respond to market pressures and evolve into a much more mature product. Something that will survive contact with early users and adapt fast based on what they push it toward. It is freeing to know that the product will change a lot, so it does not have to be perfect from the start. Ultimately, what it becomes will depend on where you begin and who you begin it with.