Why most AI products fail: Lessons from 50+ AI deployments at OpenAI, Google, and Amazon

We worked on a guest post together that this really key insight that building AI products is Very different from building non-AI products. Most people tend to ignore the non-determinism. You don't know how the user might behave with your product, and you also don't know how the LLM might respond to that. The second difference is the agency control trade-off. Every time you hand over decision-making capabilities to agentix systems, you're kind of relinquishing some amount of control on your This significantly changes the way you should be building product. So we recommend building step by step. When you start small, it forces you to think about what is the problem that I'm gonna solve. In all this advancements of the AI one easy slippery slope is to keep thinking about complexities of the solution and forget the problem that you're trying to solve. It's not about being the first company to have an agent. Among your competitors. It's about have you built the right flywheels in place so that you can improve over time. What kind of ways of working do you see in companies that build AI products successfully? I used to work with the CEO of now Rackspace. He would have this block every day in the morning, which would say catching up with AI four to six AM. Leaders have to get back to to being hands on, you must be comfortable with the fact that your intuition might not be right. And you probably are the dumbest person in the room and you want to learn from everyone.

is gonna look like persistence is extremely valuable. Successful companies right now building in any new area. They are going through the pain of learning this, implementing this, and understanding what works and what doesn't work. Pain is the new mode. Today my guests are Ashwarya Raganti and Kuriti Bottom. Three T works on codecs at OpenAI. And has spent the last decade building AI and ML infrastructure at Google and at Kumo. Ash was an early AI researcher at Alexa and Microsoft. And has published over thirty five research papers.

Together, they've led and supported over fifty AI product deployments across companies like Amazon, Databricks, OpenAI, Google, and both startups and large enterprises. Together, they also teach the number one rated AI course on Maven. Where they teach product leaders all of the key lessons they've learned about building successful AI products. The goal of this episode is to save you and your team a lot of pain and suffering and wasted time trying to build your AI product. Whether you are already struggling to make your product work or want to avoid that struggle, this episode is for you. If you enjoyed this podcast, don't forget to subscribe and follow to your favorite podcasting app or YouTube. It helps tremendously. And if you become an annual subscriber of my newsletter, you get a year free of a ton of incredible products, including a year free of lovable, replit, bulk, gamma, N8M, Linear, Devon, Posthoc, Superhuman, Descript, Whisperflow, Perplexity, Warp Granola Magic Pattern, Dreycast, Chapter D Mobbed, and Stripe Atlas. Head on over to Lenny's Newsletter.com and click Product Pass.

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