Introduction

AI apps are growing fast, but many users leave just as fast. New data shows why retention is the real risk for AI startups.

What's the problem it solves?

Many AI companies treat all revenue as stable ARR, but a big share comes from short tests and curious users. This hides a serious churn problem that can kill long-term growth.

Quick Summary

Kyle Poyar analyzed data from 3,500 software companies with ChartMogul to compare retention across B2B SaaS, B2C SaaS, and AI-native companies. The results are rough for AI. Median AI retention is worse than B2C and far behind B2B SaaS, meaning most AI users leave within a year.

Low-priced AI tools are hit hardest. Products under $50 per month have extremely high churn because they are easy to try and easy to cancel. Many users pay out of curiosity, not because the product is part of a real workflow.

The data shows a clear link between retention and long-term growth. AI companies that survive and scale improve retention over time by moving upmarket, focusing on serious use cases, and embedding deeper into customer workflows.

Key Takeaways

• AI-native companies have much worse retention than B2B SaaS.
• Cheap, self-serve AI tools churn the fastest.
• Many AI buyers are tourists, not long-term users.
• Retention improves when AI products move from experiments to real business use.
• Strong retention is required for durable growth and real ARR.

What to do

• Focus on high-value workflows with real budgets.
• Move upmarket and sell to teams, not just individuals.
• Price for seriousness, not curiosity.
• Add services or onboarding help to drive real adoption.
• Sell annual plans and make them the default.
• Stop overselling big promises before proving value.
• Track durable revenue separately from experimental spend.

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