Counterintuitive advice for building AI products
Counterintuitive advice for building AI productsLessons from 20+ top product builders, including Scott Belsky, Elad Gil, Rahul Vohra, and Sarah Guo
👋 Hey, I’m Lenny and welcome to a 🔒 subscriber-only edition 🔒 of my weekly newsletter. Each week I tackle reader questions about building product, driving growth, and accelerating your career. For more: Best of Lenny’s Newsletter | Hire your next product leader | Podcast | Lennybot | Swag You’re either building AI into your product now or you will be soon. And you’re probably already swimming in advice on the subject. But most of the advice out there is full of big, lofty ideas and light on tactics rooted in actual experience that you can implement today. So I teamed up with frequent collaborator Kyle Poyar to interview more than 20 successful builders and founders—people who have learned about building AI products the hard way—to share their biggest surprises and counterintuitive lessons. Many of these insights surprised me and got me thinking in a different way. I hope they will for you too. Let me guess: You have a high-priority project on your roadmap right now to add (more) AI features to your product. You’re in good company. A recent survey by Emergence Capital found that 60% of companies have already integrated generative AI into their products, and another 24% have it on their roadmap. AI is quickly eating the world. Unfortunately, many of these efforts will end in failure. Most early AI apps have a “tourist” problem: they get a lot of traction quickly but have shockingly low retention and engagement. And according to that Emergence survey, two in five gen AI products still haven’t made a single dollar despite companies spending millions (or even billions) to build and support them. The goal of this post is to help you avoid wasting your precious time and resources by pointing your team in the wrong direction. I polled more than 20 of the sharpest AI product builders asking them for the most counterintuitive and surprising things they’ve learned about building AI into their products. These leaders have built many of today’s most loved and successful AI products, including products at Adobe, GitHub, Intercom, Perplexity, Canva, Runway, HeyGen, and Superhuman. Here’s what I learned: 1. You first need to learn to think differently
2. Though it still comes down to who can solve real problems for people best
3. Paired with the right product design and UX to teach people how to use it
4. And the (proprietary) data you have access to (and a license for) is becoming increasingly important
5. Be intentional about your initial wedge workflow...Subscribe to Lenny's Newsletter to unlock the rest.Become a paying subscriber of Lenny's Newsletter to get access to this post and other subscriber-only content. A subscription gets you:
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