a16z访谈Gamma与Town：消费级AI产品的增长与定价心得

阅读原文：https://t.co/8cNvNq99qL

阅读原文： x.com/omooretweets/s… Olivia Moore @omooretweets Fun to talk with @jgreze ( @TownAI ) and Deeni Fathia ( @GammaApp ) about building a consumer AI business These two teams are best-in-class in developing delightful, retentive products that individuals adopt...and then bring into work. My takeaways: - Consumers are unlikely to pay for AI directly (via subscription). But, using consumer as a low cost enterprise funnel can be very effective, especially if you have a product where the output is naturally shareable and is used to do meaningful work. - You can't serve everyone. Some highly technical users are going to want a level of granularity / dial tuning that doesn't make sense to provide. You can lean in to this in select areas of the product where the average user does care - for Gamma, this is selecting image models. - For products that are adopted across a team, pooling tokens is one way to approach pricing. Employees will naturally have varying levels of adoption of AI tools, and allowing sharing across a team can feel more fair / give customers higher ROI (they'll be more likely to stay!) - The ecosystem resets every 2-3 months, so you need to be always reinventing yourself as a company. Gamma (which has more than 100M users) just rebuilt and relaunched its product in ~3 months to better serve how people are looking to tell stories with AI now. - Trying to beat the labs at their own game is often a mistake. Leaning into a specific angle that you feel unlocks the market (for Town, this is multiplayer) or a specific type of customer is often better than trying to take the same approach with fewer resources. 🔗 View Quoted Tweet 💬 0 🔄 0 ❤️ 0 👀 126 ⚡