How To Get Your First Customers (6 min)
ai-driven-innovation-economy ai-global-economic-shifts ai-investment-trends ai-monetization-strategies ai-utopias-vs-dystopias
- Release date: 2026-01-14
- Listen on Spotify: Open episode
- Episode description:
When you're starting out, it isn’t enough to just build a minimum viable product. You also need a minimum evolvable product - one that can adapt to the needs of those critical early customers. In this episode of Main Function, YC General Partner Ankit Gupta offers an update to the classic MVP playbook. He’ll outline strategies for getting your first customers, the power of adaptability and how feedback from early users will ultimately shape the future of your product and your company.Apply to Y Combinator: https://www.ycombinator.com/applyWork at a startup: https://www.ycombinator.com/jobsChapters:00:00 – The Minimum Evolvable Product00:46 – Finding the First Believers01:29 – Counterintuitive Rules To Get Early Users02:10 – Learn Fast, Don’t Fear Churn02:52 – How Early Users Shape the Market You Enter04:22 – Tesla Case Study05:14 – How To Build To Evolve
Summary
- 🔍 Search for Early Adopters: Finding first users is about hunting rare early adopters or those with burning needs, not mass persuasion, exemplified by quick adoption of niche startup solutions.
- 💸 Charge Real Money Early: Paying customers provide sharper feedback than free users and aren’t price-sensitive, prioritizing quality insights over revenue.
- 📈 Build Evolvable Products: Start with a ‘minimum evolvable product’ like an amoeba that adapts to early user pressures, as seen in Tesla’s path from Roadster to Model Y.
- 🎯 Targeted Outreach & Launch Fast: Use personal cold emails, launch early for wide discovery, study users anthropologically, and experiment without fearing churn.
- 🏢 Favor B2B in AI Era: AI costs challenge consumer apps due to low ad revenue and budgets, so target prosumers, businesses, or high-value users like doctors.
Insights
Why is finding the first users for an AI product more of a search problem than a persuasion challenge?
Time: 0:19 – 1:23
Category: AI-Driven Innovation EconomyAnswer: Most people aren’t early adopters, but there are ‘Gustavs’ who love trying startups or people with burning needs willing to pay unknowns, as in the speaker’s quick adoption of a startup’s inference API solution. This shifts focus from broad marketing to targeted hunting for these rare users. (Start at 0:19)
What is a ‘minimum evolvable product’ and why does it beat a traditional MVP for AI startups?
Time: 0:34 – 0:40
Category: AI-Driven Innovation EconomyAnswer: An MEP is a simple starting point designed to survive early user contact and rapidly adapt to feedback, freeing founders from perfectionism since products massively change based on initial market pressures. (Start at 0:34)
Should AI startups charge real money from their earliest users?
Time: 1:32 – 1:44
Category: AI Monetization Strategies, AI Investment TrendsAnswer: Early adopters and those with urgent problems are rarely price-sensitive, and paying customers deliver sharper, more actionable feedback than free users, prioritizing feedback quality over initial revenue. (Start at 1:32)
How can personal outreach outperform mass marketing for AI product launches?
Time: 1:49 – 2:01
Category: AI-Driven Innovation EconomyAnswer: Targeted cold emails or direct contacts are far more effective at reaching early adopters than billboards, as these users require precise, personal discovery rather than broad awareness. (Start at 1:49)
In the AI era, why target businesses or prosumers over everyday consumers?
Time: 2:52 – 3:23
Category: AI Monetization Strategies, AI & Global Economic ShiftsAnswer: AI’s high costs make consumer apps challenging, as ads rarely cover expenses and personal budgets are tiny ($150/month avg), pushing founders toward high-value users like doctors or corporate tools that spend more. (Start at 2:52)
How do early adopters irreversibly steer an AI product’s evolution?
Time: 3:23 – 5:07
Category: AI-Driven Innovation Economy, AI Utopias vs. DystopiasAnswer: Using a phylogenetic tree analogy, startups start as ‘amoebas’ and evolve path-dependently based on early user preferences, like Tesla’s Roadster attracting acceleration enthusiasts, resulting in Model Y’s sporty specs over comfort. (Start at 3:23)